Portal Jurnal Ilmiah
Goretan Pena

Abstract : The development of information technology and social media has made it easier for people to express their opinions on various issues, including the government’s responsiveness in handling flood disasters. The large number of comments generated on social media makes manual opinion analysis ineffective because it requires a long time and involves a large volume of data. Therefore, an automated method is needed to process and classify public sentiment. This study aims to implement text mining to analyze public sentiment toward the government’s responsiveness in flood disaster management using the K-Nearest Neighbour (KNN) algorithm. The research data were collected from social media platforms such as Instagram, TikTok, and X, consisting of public comments related to flood disasters in Aceh and North Sumatra. The research stages include data collection, text preprocessing, feature extraction, data splitting into training and testing sets, sentiment classification using the KNN method, and evaluation of classification results. The system was developed as a web-based application using PHP programming language and MySQL database. The results of this study are expected to provide an overview of public opinion tendencies in the form of positive, negative, and neutral sentiments and serve as evaluation material for the government to improve its response in flood disaster management. Keywords: Text Mining, Sentiment Analysis, K-Nearest Neighbour (KNN), Social Media, Flood Disaster, Government Responsiveness.   Abstrak : Perkembangan teknologi informasi dan media sosial menyebabkan masyarakat semakin mudah menyampaikan pendapat terhadap berbagai isu, termasuk ketanggapan pemerintah dalam penanggulangan bencana banjir. Banyaknya komentar yang muncul di media sosial menjadikan analisis opini secara manual tidak efektif karena membutuhkan waktu yang lama dan jumlah data yang besar. Oleh karena itu, diperlukan metode otomatis untuk mengolah dan mengklasifikasikan sentimen masyarakat. Penelitian ini bertujuan untuk mengimplementasikan text mining dalam menganalisis sentimen masyarakat terhadap ketanggapan pemerintah dalam penanggulangan bencana banjir menggunakan algoritma K-Nearest Neighbour (KNN). Data penelitian diperoleh dari platform media sosial Instagram, TikTok, dan X yang berisi komentar masyarakat terkait bencana banjir di wilayah Aceh dan Sumatera Utara. Tahapan penelitian meliputi pengumpulan data, preprocessing teks, ekstraksi fitur, pembagian data training dan testing, proses klasifikasi menggunakan metode KNN, serta evaluasi hasil klasifikasi. Sistem dibangun berbasis web menggunakan bahasa pemrograman PHP dan database MySQL. Hasil penelitian diharapkan dapat memberikan gambaran kecenderungan opini masyarakat dalam bentuk sentimen positif, negatif, dan netral serta membantu sebagai bahan evaluasi bagi pemerintah dalam meningkatkan kualitas respon terhadap bencana banjir. Kata kunci: : Text Mining, Analisis Sentimen, K-Nearest Neighbour (KNN), Media Sosial, Bencana Banjir, Ketanggapan Pemerintah.

Abstract: Arabica coffee is a high-value export commodity for the Indonesian economy. To maintain global competitiveness, coffee beans must meet export feasibility quality standards based on the cupping score from the Coffee Quality Institute (CQI). This study aims to compare the performance of the K-Nearest Neighbor (KNN) and Gaussian Naive Bayes algorithms in classifying the export feasibility of Arabica coffee beans. In sensory quality testing, high-dimensional attributes (10 parameters) can cause accuracy instability and increase computational load. Therefore, Correlation-based Feature Selection (CFS) was applied to reduce redundant features. The CFS process filtered the initial 10 features into 4 selected features (Flavor, Acidity, Cupper Points, and Aroma). The Dataset was obtained from the Kaggle Coffee Quality Database and had undergone Excel format adjustments before being loaded into the system. Modeling was conducted using 1,303 data records with a 30% split for testing data. The evaluation results showed that the Naive Bayes algorithm provided the best performance, achieving an accuracy rate of 97.95%. The use of CFS proved successful in reducing feature dimensions by 60% without significantly decreasing classification accuracy. Keywords: Arabica Coffee; Export Classification; K-Nearest Neighbor; Naive Bayes; Correlation-based Feature Selection.   Abstrak: Kopi Arabika merupakan komoditas ekspor bernilai tinggi bagi perekonomian Indonesia. Untuk menjaga daya saing di tingkat global, biji kopi harus memenuhi standar kualitas kelayakan ekspor berdasarkan cupping score dari Coffee Quality Institute (CQI). Penelitian ini bertujuan membandingkan kinerja algoritma K-Nearest Neighbor (KNN) dan Gaussian Naive Bayes dalam klasifikasi kelayakan ekspor biji kopi Arabika. Dalam pengujian sensoris mutu, dimensionalitas atribut yang tinggi (10 parameter) dapat menyebabkan ketidakstabilan akurasi dan meningkatkan beban komputasi. Oleh karena itu, Correlation-based Feature Selection (CFS) diterapkan untuk mereduksi fitur redundan. Proses CFS menyaring 10 fitur awal menjadi 4 fitur terpilih (Flavor, Acidity, Cupper Points, Aroma). Dataset diambil dari Kaggle Coffee Quality Database dan telah melalui tahapan penyesuaian format Excel sebelum dimuat ke sistem. Pemodelan dilakukan menggunakan 1303 rekam data dengan pembagian data uji sebesar 30%. Hasil evaluasi menunjukkan bahwa algoritma Naive Bayes memberikan performa terbaik dengan tingkat akurasi mencapai 97,95%. Penggunaan CFS terbukti berhasil memangkas dimensi fitur sebesar 60% tanpa menurunkan akurasi klasifikasi secara signifikan. Kata Kunci: Kopi Arabika; Klasifikasi Ekspor; K-Nearest Neighbor; Naive Bayes; Correlation-based Feature Selection.

Abstract: This research aims to design and build a web-based Inventory Information System for the Strategic Planning and Container Terminal Services Division of PT. Pelabuhan Indonesia (Persero). This system was developed to address the problem of manual inventory recording using Microsoft Excel, which is prone to recording errors, data loss, and difficulties in preparing reports and monitoring goods in real time. The system development method used is the System Development Life Cycle (SDLC) with a Prototype model. The result of this research is a web application that allows for structured recording of incoming and outgoing goods, as well as requests for borrowing goods. Key features include master data management (goods, suppliers, shelves, officers, admins), automated stock updates, an information dashboard, and report generation. System testing using the Black Box Testing method showed that all system functions were running well and in accordance with user needs. The implementation of this system is expected to improve operational efficiency, data accuracy, and support faster and more accurate decision-making processes within the company. Keywords: Information System, Inventory, Website, SDLC Prototype, Black Box Testing, PT. Pelabuhan Indonesia.   Abstrak: Penelitian ini bertujuan untuk merancang dan membangun Sistem Informasi Inventaris Barang berbasis web pada Divisi Perencanaan Strategis dan Pelayanan Terminal Petikemas PT. Pelabuhan Indonesia. Sistem ini dikembangkan untuk mengatasi permasalahan pencatatan inventaris yang masih dilakukan secara manual menggunakan Microsoft Excel, yang rentan terhadap kesalahan pencatatan, kehilangan data, serta kesulitan dalam penyusunan laporan dan pemantauan barang secara real-time. Metode pengembangan sistem yang digunakan adalah System Development Life Cycle (SDLC) dengan model Prototype. Hasil dari penelitian ini berupa aplikasi web yang memungkinkan pencatatan barang masuk, keluar, dan pengajuan peminjaman barang secara terstruktur. Fitur-fitur utama mencakup manajemen data master (barang, supplier, rak, petugas, admin), otomatisasi pembaruan stok, dashboard informasi, serta pembuatan laporan. Pengujian sistem menggunakan metode Black Box Testing menunjukkan bahwa seluruh fungsi sistem telah berjalan dengan baik dan sesuai dengan kebutuhan pengguna. Implementasi sistem ini diharapkan dapat meningkatkan efisiensi operasional, akurasi data, serta mendukung proses pengambilan keputusan yang lebih cepat dan tepat di lingkungan perusahaan. Kata Kunci: Sistem Informasi, Inventaris Barang, Website, SDLC Prototype, Black Box Testing, PT. Pelabuhan Indonesia.

Abstract: The waters of Tulungagung Regency possess potential for clear lobster seed (BBL), driving high levels of fishing activity for this commodity. BBL management—including harvesting activities—is governed by government regulations that underwent frequent changes between 2021 and 2024. This study aimed to examine BBL management issues in Tulungagung by characterizing BBL utilization, assessing the sustainability status of BBL management, and formulating management strategies. Data were collected through interviews, observation, and documentation. Analysis employed MDS RAPFISH to determine sustainability status and AHP to formulate priority management strategies. The results indicate that BBL fishing activities in Tulungagung are environmentally friendly, utilizing *pocong* nets that comply with the standards set in the Regulation of the Minister of Marine Affairs and Fisheries Number 7 of 2024. RAPFISH analysis classified the sustainability status as "sustainable," reflecting controlled utilization levels alongside relatively stable fishing volumes and activity among local fishers. Meanwhile, AHP analysis identified the implementation of a closed season—based on peak lobster spawning periods—as the primary management strategy for BBL in Tulungagung Regency. This study recommends this priority strategy as the key measure to improve and maintain the sustainability of BBL management in the region. Keywords: Sustainability, lobster spawning season, MDS RAPFISH, AHP   Abstrak: Perairan Kabupaten Tulungagung memiliki potensi benih bening lobster (BBL) yang mendorong tingginya aktivitas penangkapan komoditas tersebut. Pengelolaan BBL termasuk di dalamnya aktivitas penangkapan telah diatur dalam peraturan pemerintah yang sering mengalami perubahan pada kurun waktu 2021 hingga 2024. Penelitian ini dilakukan dengan beberapa tujuan guna mendalami persoalan pengelolaan BBL khususnya di Tulungagung diantaranya menentukan keragaan karakteristik pemanfaatan BBL, menetapkan status keberlanjutan pengelolaan BBL, serta menentukan strategi pengelolaan BBL. Teknik pengumpulan data berupa wawancara, observasi dan dokumentasi. Analisis data yang digunakan MDS RAPFISH untuk penetapan status keberlanjutan dan AHP perumusan strategi prioritas pengelolaan BBL. Hasil penelitian menunjukkan bahwa kegiatan penangkapan BBL yang di Tulungagung tergolong ramah lingkungan menggunakan alat berupa jaring pocong yang masih sesuai dengan kaidah yang ditetapkan dalam Permen KP Nomor 7 Tahun 2024. Hasil analisis RAPFISH menunjukkan status keberlanjutan pada kategori berkelanjutan yang menunjukkan tingkat pemanfaatan yang masih terkontrol dengan relatif stabilnya jumlah dan aktivitas penangkapan oleh nelayan setempat. Sedangkan pada hasil analisis prioritas rekomendasi kebijakan (AHP) diperoleh strategi penerapan musim tertutup berdasarkan puncak pemijahan lobster sebagai pilihan strategi pengelolaan BBL paling utama di Kabupaten Tulungagung. Studi ini merekomendasikan strategi prioritas tersebut sebagai upaya utama untuk memperbaiki dan mempertahankan status keberlanjutan pengelolaan BBL di Tulungagung. Kata kunci: Keberlanjutan, BBL, MDS RAPFISH, AHP

Abstract: The first goal of the Sustainable Development Goals (SDGs), namely poverty eradication, is a major priority of Indonesia’s national development agenda. Public responses to poverty alleviation initiatives provide valuable insights for assessing the effectiveness of government policies. This study examines public sentiment regarding the implementation of SDG 1 using posts collected from the X platform and compares the classification performance of Support Vector Machine (SVM) and IndoBERT models. A dataset consisting of 1,002 Indonesian-language posts was gathered through web scraping and Application Programming Interface (API) techniques. The research process included data preprocessing, sentiment labeling through a lexicon-based approach, TF-IDF feature extraction for the SVM model, and fine-tuning for the IndoBERT model. Model performance was evaluated using a confusion matrix with accuracy, precision, recall, and F1-score metrics. The findings indicate that negative sentiment is more dominant than positive sentiment toward poverty alleviation programs. The SVM model achieved an accuracy of 80.60%, while IndoBERT reached 80.10%. These results suggest that SVM performed slightly better on the dataset used in this study. Overall, the findings demonstrate that artificial intelligence-based sentiment analysis can support monitoring public perceptions and evaluating poverty reduction policies in Indonesia more effectively and comprehensively for evidence-based decision making processes nationwide. Keywords: sentiment analysis; SDG 1; poverty; SVM; IndoBERT.   Abstrak: Tujuan pertama Sustainable Development Goals (SDGs), yaitu menghapus kemiskinan, menjadi salah satu agenda strategis pembangunan nasional Indonesia. Respons masyarakat terhadap berbagai program pengentasan kemiskinan dapat digunakan sebagai bahan evaluasi terhadap efektivitas kebijakan yang diterapkan pemerintah. Penelitian ini menganalisis sentimen publik mengenai implementasi SDG 1 dengan memanfaatkan unggahan pada platform X serta membandingkan kinerja model Support Vector Machine (SVM) dan IndoBERT dalam proses klasifikasi. Sebanyak 1.002 unggahan berbahasa Indonesia dikumpulkan melalui teknik scraping dan pemanfaatan API. Tahapan penelitian meliputi praproses data, pemberian label sentimen menggunakan pendekatan leksikon, pembentukan fitur TF-IDF pada SVM, serta fine-tuning model IndoBERT. Kinerja model dievaluasi menggunakan confusion matrix dengan indikator accuracy, precision, recall, dan F1-score. Hasil penelitian memperlihatkan bahwa opini negatif lebih dominan dibandingkan opini positif terhadap program pengentasan kemiskinan. Model SVM memperoleh tingkat akurasi sebesar 80,60%, sedangkan IndoBERT mencapai 80,10%. Temuan tersebut menunjukkan bahwa SVM memiliki performa yang sedikit lebih unggul pada dataset yang digunakan. Penelitian ini mengindikasikan bahwa analisis sentimen berbasis kecerdasan buatan berpotensi menjadi alat pendukung dalam memonitor persepsi masyarakat serta mengevaluasi kebijakan pengurangan kemiskinan di Indonesia. Kata kunci: analisis sentimen; SDG 1; kemiskinan; SVM; IndoBERT.

Abstract: The availability of clean water is a basic need that significantly impacts the health and quality of life of the community, especially in rural areas such as Sei Kepayang District, which still relies on well water and surface air sources with quality that does not always meet health standards, such as high turbidity levels and unsuitable air pH. The need for clean water for the Indonesian people is very important. Whether it is used for drinking water, cooking, bathing and washing. However, this need for clean water is very difficult to meet in areas far from water sources, especially private wells. Most air sources obtained from wells are often cloudy and the water pH does not match the nominal limit. One example is in Sei Kepayang District. Many residents still use rain-fed wells whose water quality is not always good. Given this problem, a solution that can be applied is to monitor air quality to determine the good or bad air quality so that it can be used by the community. The implementation of IoT (internet of things) air quality monitoring and administration systems in small-scale clean water management that displays conditions visually on the monitoring feature can be controlled efficiently with wireless media via the website. Furthermore, the addition of a sensor to detect and filter air pH levels can help communities struggling to access clean air and determine whether it is fit for consumption. The monitoring website also displays monthly PDAM usage costs, obtained from flow meter sensor data. This development falls under small-scale Smart City management, as it helps communities modernize their daily appliances. The implementation of the IoT (Internet of Things) air quality monitoring and administration system involved testing each sensor. Tests were conducted on each device. The test results showed that the pH sensor obtained a value of 7.00 for mineral water, 5.9 for lemon juice, and 10.4 for soapy water, and a litmus test for acid-base determination. Tests on the turbidity sensor for clean water yielded an average error of 0.12%, 0.02% for tea, and 0.08% for coffee.   Keywords: Internet of Things, air quality monitoring, Administration, pH meter, turbidity, flow meter, Arduino uno, Smart City, ESP8266. Abstrak: Ketersediaan air bersih merupakan kebutuhan mendasar yang sangat berpengaruh terhadap kesehatan dan kualitas hidup masyarakat, khususnya di wilayah pedesaan seperti Kecamatan Sei Kepayang yang masih bergantung pada sumber air sumur dan air permukaan dengan kualitas yang tidak selalu memenuhi standar kesehatan, seperti tingkat kekeruhan yang tinggi dan pH air yang tidak sesuai. Kebutuhan air bersih untuk masyarakat Indonesia sangatlah penting. Baik itu digunakan untuk air minum , memasak, mandi dan mencuci. Namun kebutuhan air bersih ini sangat sulit di penuhi di daerah yang jauh dari sumber mata air, terlebih lagi sumur-sumur milik pribadi. Kebanyakan sumber air yang diperoleh dari sumur sering sekali keruh dan pH airnya tidak sesuai dari batas nomal. Salah satunya di Kecamatan Sei Kepayang. Masih banyak penduduknya menggunakan sumur tadah hujan yang kualitas airnya tidak selalu baik. Dengan adanya masalah tersebut, solusi yang dapat diterapkan adalah monitoring kualitas air untuk menentukan baik buruknya kualitas air agar bisa digunakan oleh masyarakat. Dikembangkan implementasi IoT (internet of things) monitoring kualitas air dan sistem administrasi pada pengelola air bersih skala kecil yang menampilkan keadaan secara visual pada fitur monitoring dapat dikendalikan secara efisien dengan media wireless melalui website. Disamping itu dengan ditambahkannya sebuah sensor untuk mendeteksi kadar pH air dan penyaringannya dapat membantu penduduk yang sulit mendapatkan air bersih maupun menentukan bahwa air tersebut layak tidaknya untuk di konsumsi. Serta menampilkan biaya penggunaan PDAM dalam kurun waktu bulanan pada website monitoring yang didapatkan dari data sensor flow meter. Pengembangan ini termasuk dalam pengelolaan Smart City skala kecil, karena pengembangan alat ini membantu penduduk untuk lebih moderenisasi alat-alat dalam kehidupan sehari- hari . Dari Implementasi IoT (internet of things) monitoring kualitas air dan sistem administrasi dilakukan pengujian pada Setiap alat yang dilakukan pada setiap sensor yang di gunakan pada tiap alat. Diperoleh Hasil pengujian menunjukkan bahwa sensor pH didapatkan nilai 7.00 untuk air mineral, air lemon 5.9, air sabun 10.4, dan dengan indikasi lakmus dalam penentuan asam basa pada air. Pengujian diperoleh pada sensor turbidity terhadap air bersih didapatkan nilai rata-rata error 0.12%, teh 0.02%, dan air kopi 0.08%. Kata kunci : Internet of Things , monitoring kualitas air, Administrasi,pH meter, turbidity,flow meter , Arduino                       uno,Smart City,ESP8266.  

RSGM Baiturrahmah serves as a medical institution that generates a high volume of daily patient records. However, this wealth of data has not been optimally utilized by management as a primary consideration for strategic decision-making. The identified core problem is the absence of comprehensive patient characteristic mapping, which often leads to an uneven distribution of medical resources. To address this critical issue, this study applies advanced data mining techniques using the K-Means Clustering algorithm to group 1,708 dental and oral disease patient records. The clustering process was conducted by determining three main clusters based on three crucial attributes patient age, the total number of diagnoses received, and the duration of medical service provided. The results of this study successfully classify all patients into three specific service categories, namely Basic Service, Intermediate Service, and Intensive Service. This research also produced a comprehensive web-based decision support system developed using the Python programming language and MySQL database. The system is specifically designed to assist the management of RSGM Baiturrahmah in accurately visualizing the characteristics of each patient group. With the successful implementation of this system, hospital management can be more effective in formulating highly personalized and targeted service strategies for every patient group.

Oil palm is one of the plantation commodities that plays an important role in improving Indonesia's economy. PT Socfindo Kebun Aek Loba has a large amount of oil palm plant condition data; however, the data has not been optimally utilized to determine fertilizer dosage requirements. This study aims to classify oil palm fertilizer dosage requirements based on plant conditions using the K-Means algorithm and to design an application that supports the clustering process. The variables used in this study include plant age, tree height, number of fruit bunches, and number of fronds. The dataset consisted of 200 oil palm plant records. The clustering process was carried out by forming three clusters, namely low, medium, and high fertilizer dosage groups, using the K-Means method with Euclidean Distance calculations. The results showed that out of 200 plant data records processed, 92 data (46%) were classified into the low fertilizer dosage cluster, 54 data (27%) into the medium fertilizer dosage cluster, and 54 data (27%) into the high fertilizer dosage cluster. In addition, this study successfully developed an application using PHP and MySQL that is capable of managing data, performing clustering automatically, and presenting clustering results in the form of tables and charts. The developed application can assist PT Socfindo Kebun Aek Loba in obtaining information regarding fertilizer dosage requirements more quickly, effectively, and systematically, thereby supporting decision-making related to oil palm fertilization.

Abstract: Illegal, Unreported, and Unregulated Fishing (IUU Fishing) is one of the fisheries-related crimes that threatens the sustainability of marine resources, causes significant economic losses to the state, and undermines Indonesia's maritime sovereignty. The North Natuna Sea Exclusive Economic Zone (EEZ) is particularly vulnerable to such practices due to its abundant fishery resources and strategic geographical location. This study aims to analyze the national legal framework governing the prevention and eradication of IUU Fishing in the North Natuna Sea EEZ, identify the challenges faced by Indonesia in combating such activities, and examine the role of national and international law in promoting marine environmental sustainability. This research employs a normative juridical method with a descriptive-analytical approach. The data used consist of secondary data obtained through library research on primary, secondary, and tertiary legal materials related to international maritime law and national fisheries law. The data were analyzed qualitatively through the examination of legislation and relevant legal literature. The results indicate that Indonesia has established a relatively comprehensive legal framework for combating IUU Fishing through various laws and regulations that incorporate principles of international law. The synergy between national legal instruments and the provisions of UNCLOS 1982 plays a crucial role in strengthening law enforcement, protecting fishery resources, safeguarding national sovereignty, and supporting the sustainability of the marine environment. Keywords: IUU Fishing, Exclusive Economic Zone, Marine Environmental Sustainability   Abstrak: Illegal, Unreported, and Unregulated Fishing (IUU Fishing) merupakan salah satu bentuk kejahatan perikanan yang mengancam keberlanjutan sumber daya laut, menimbulkan kerugian ekonomi negara, serta mengganggu kedaulatan wilayah perairan Indonesia. Zona Ekonomi Eksklusif (ZEE) Laut Natuna Utara menjadi kawasan yang rentan terhadap praktik tersebut karena memiliki potensi sumber daya perikanan yang besar dan posisi strategis. Penelitian ini bertujuan untuk menganalisis pengaturan hukum nasional terkait penanggulangan IUU Fishing di ZEE Laut Natuna Utara, mengidentifikasi kendala yang dihadapi Indonesia, serta mengkaji peran hukum nasional dan internasional dalam mewujudkan keberlanjutan lingkungan laut. Penelitian ini menggunakan metode yuridis normatif dengan pendekatan deskriptif analitis. Data yang digunakan berupa data sekunder yang diperoleh melalui studi kepustakaan terhadap bahan hukum primer, sekunder, dan tersier yang berkaitan dengan hukum laut internasional dan hukum perikanan nasional. Analisis dilakukan secara kualitatif melalui penelaahan peraturan perundang-undangan dan literatur yang relevan. Hasil penelitian menunjukkan bahwa Indonesia telah memiliki landasan hukum yang cukup komprehensif dalam penanggulangan IUU Fishing melalui berbagai peraturan perundang-undangan yang mengadopsi prinsip-prinsip hukum internasional. Sinergi antara hukum nasional dan ketentuan UNCLOS 1982 berperan penting dalam memperkuat penegakan hukum, melindungi sumber daya perikanan, menjaga kedaulatan negara, serta mendukung keberlanjutan lingkungan laut. Kata kunci: IUU Fishing, Zona Ekonomi Eksklusif, Keberlanjutan Lingkungan Laut

Abstract: The unlawful occupation and conversion of conservation forest areas constitute acts that not only cause state financial losses but also result in environmental degradation and the loss of conservation functions. This study aims to analyze the legal regulation of corruption crimes related to the occupation and conversion of conservation forest areas and to examine judicial considerations in Medan District Court Decision Number 138/Pid.Sus-TPK/2024/PN Mdn. This research employs a normative legal research method using statutory, conceptual, and case approaches. Data were collected through library research on primary, secondary, and tertiary legal materials and analyzed qualitatively. The results indicate that the legal framework governing corruption crimes in the forestry sector is relatively comprehensive; however, challenges remain regarding unclear legal norms and regulatory disharmony. The judges’ considerations in the case were based on the legal status of the conservation forest area, the existence of unlawful acts, state losses, and the environmental damage caused by the defendant’s actions. The court concluded that the defendant had fulfilled the elements of corruption, and therefore the decision was not solely based on juridical considerations but also on environmental protection and broader public interests. This study highlights the importance of strict law enforcement against corruption in the forestry sector to ensure legal certainty, environmental protection, and sustainable natural resource management. Keywords: Corruption Crime; Conservation Forest; Judicial Consideration; State Losses; Environmental Protection   Abstrak: Penguasaan dan pengalihfungsian kawasan hutan konservasi secara melawan hukum merupakan perbuatan yang tidak hanya menimbulkan kerugian keuangan negara, tetapi juga berdampak pada kerusakan lingkungan hidup dan hilangnya fungsi konservasi. Penelitian ini bertujuan untuk menganalisis pengaturan hukum tindak pidana korupsi dalam penguasaan dan pengalihfungsian kawasan hutan konservasi serta menganalisis pertimbangan hakim dalam Putusan Pengadilan Negeri Medan Nomor 138/Pid.Sus-TPK/2024/PN Mdn. Metode penelitian yang digunakan adalah penelitian hukum normatif dengan pendekatan perundang-undangan, pendekatan konseptual, dan pendekatan kasus. Data diperoleh melalui studi kepustakaan terhadap bahan hukum primer, sekunder, dan tersier yang dianalisis secara kualitatif. Hasil penelitian menunjukkan bahwa pengaturan hukum mengenai tindak pidana korupsi di sektor kehutanan telah memiliki dasar hukum yang kuat, namun masih menghadapi kendala berupa ketidakjelasan norma dan disharmonisasi peraturan. Pertimbangan hakim dalam putusan a quo didasarkan pada pembuktian status kawasan hutan konservasi, adanya perbuatan melawan hukum, kerugian negara, serta dampak kerusakan lingkungan hidup yang ditimbulkan. Hakim menilai bahwa tindakan terdakwa terbukti memenuhi unsur tindak pidana korupsi sehingga putusan tidak hanya berorientasi pada aspek yuridis, tetapi juga memperhatikan perlindungan lingkungan hidup dan kepentingan masyarakat. Penelitian ini menegaskan pentingnya penegakan hukum yang tegas terhadap tindak pidana korupsi di sektor kehutanan guna menjamin kepastian hukum, perlindungan lingkungan, dan keberlanjutan sumber daya alam. Kata kunci: Tindak Pidana Korupsi; Hutan Konservasi; Pertimbangan Hakim; Kerugian Negara; Lingkungan Hidup

Abstract: This study aims to develop a digital storybook based on Batak Toba culture integrated with QuillBot AI as an editing assistant to support students' French writing skills. The study employed a mixed-methods approach using the Educational Design Research (EDR) framework. Data were collected through interviews, observations, expert validation questionnaires, and limited trials. The resulting product was a digital storybook entitled Sahabat Danau Toba, equipped with writing tasks, a French glossary, and a QuillBot-assisted editing feature. Material and media expert validations achieved scores of 94% and 93%, respectively, indicating that the product was highly feasible. The one-to-one, small-group, and field-test evaluations obtained scores of 83.3%, 88%, and 96%, respectively. The overall user response reached 94.3% and was categorized as very good. Students reported that the Batak Toba-based story was interesting, easy to understand, and helpful in generating writing ideas. QuillBot also helped students independently revise their grammar and improve their writing. Therefore, the developed product is feasible as a supporting medium for French writing instruction based on local culture. Keywords: Artificial Intelligence, Batak Toba Culture, Digital Storybooks, Writing Skills, French Language Learning   Abstrak: Penelitian ini bertujuan mengembangkan buku cerita digital berbasis budaya Batak Toba yang terintegrasi dengan AI QuillBot sebagai asisten editor untuk mendukung keterampilan menulis bahasa Prancis mahasiswa. Penelitian menggunakan pendekatan mixed methods dengan desain Educational Design Research (EDR). Data dikumpulkan melalui wawancara, observasi, angket validasi ahli, dan uji coba terbatas. Produk yang dihasilkan berupa buku cerita digital Sahabat Danau Toba yang dilengkapi tugas menulis, glosarium bahasa Prancis, dan fitur QuillBot. Hasil validasi ahli materi dan ahli media masing-masing memperoleh skor 94% dan 93% dengan kategori sangat layak. Hasil one-to-one evaluation memperoleh skor 83,3%, small group evaluation 88%, dan field test 96%. Rata-rata respons pengguna mencapai 94,3% dengan kategori sangat baik. Mahasiswa menyatakan bahwa cerita berbasis budaya Batak Toba menarik, mudah dipahami, dan membantu mengembangkan ide menulis. Fitur QuillBot juga membantu mahasiswa memperbaiki tata bahasa dan tulisan secara mandiri. Dengan demikian, produk yang dikembangkan layak digunakan sebagai media pendukung pembelajaran menulis bahasa Prancis berbasis budaya lokal. Kata kunci: Artificial Intelligence, Budaya Batak Toba, Buku Cerita Digital, Writing Skills, French Language Learning

Abstract: The development of Industry 4.0 toward Society 5.0 requires innovation in vocational learning media based on Internet of Things (IoT) for motorcycle electrical systems. This study aims to develop a learning media for real-time monitoring and fault diagnosis. The method used is Design Based Research (DBR) consisting of analysis, design, development, and testing stages. The system integrates an ADC voltage sensor (12.6 V normal; ADC ≈ 859 and 12.1 V weak; ADC ≈ 820), pull-up resistor switch detection (logic 0 = 0 V normal; logic 1 = 5 V fault), ACS712 current sensor (1.5 A lamp ON; ADC ≈ 568 and 0 A fuse fault; ADC ≈ 511), and INA219 charging sensor (14.0 V normal charging; ADCbus ≈ 3500 and 12.4 V not charging; ADCbus ≈ 3100). The results show that all sensors operate stably and accurately in detecting both normal and fault conditions. Integration into a web-based dashboard enables real-time monitoring via computers and smartphones. Therefore, the developed learning media effectively improves students’ understanding, visualization, and analytical skills in IoT-based motorcycle electrical systems. Keywords: ACS712; ADC; Design Based Research; INA219; IoT; Motorcycle Electrical System; Learning Media   Abstrak: Perkembangan Revolusi Industri 4.0 menuju Society 5.0 menuntut inovasi media pembelajaran vokasi berbasis Internet of Things (IoT) pada sistem kelistrikan sepeda motor. Penelitian ini bertujuan mengembangkan media pembelajaran untuk monitoring dan diagnosis kerusakan kelistrikan secara real-time. Metode yang digunakan adalah Design Based Research (DBR) melalui tahap analisis, perancangan, pengembangan, dan pengujian. Sistem menggunakan sensor ADC (12,6 V normal; ADC ≈ 859 dan 12,1 V lemah; ADC ≈ 820), pull-up resistor (logika 0 = 0 V normal; logika 1 = 5 V kerusakan), ACS712 (1,5 A lampu ON; ADC ≈ 568 dan 0 A saat sekring putus; ADC ≈ 511), serta INA219 (14,0 V charging normal; ADCbus ≈ 3500 dan 12,4 V tidak mengisi; ADCbus ≈ 3100). Hasil pengujian menunjukkan seluruh sensor bekerja stabil, akurat, dan sesuai fungsi dalam mendeteksi kondisi normal maupun fault pada sistem kelistrikan. Integrasi data sensor ke dashboard web memungkinkan pemantauan real-time melalui perangkat komputer dan smartphone. Dengan demikian, media pembelajaran yang dikembangkan efektif meningkatkan pemahaman, visualisasi, dan kemampuan analisis peserta didik pada sistem kelistrikan sepeda motor berbasis IoT.. Kata kunci: ACS712; ADC; Design Based Research; INA219; IoT; Kelistrikan Sepeda Motor; Media  Pembelajaran

Abstract: The massive growth of internet penetration in Indonesia has created competitive competition among internet service providers (ISPs), including Biznet Medan. However, the highly diverse and heterogeneous characteristics of customers make one-size-fits-all marketing strategies ineffective and inefficient. Processing large customer data for segmentation is often hampered by the presence of outliers, such as extreme income variations, which can disrupt the clustering accuracy of conventional algorithms. This study aims to optimize the K-Means algorithm in segmenting Biznet Medan's target market to maintain accuracy despite outliers, allowing the company to design more targeted marketing strategies. To overcome K-Means' sensitivity to outliers, this study applies the Robust Scaling technique. This solution uses the Median and Interquartile Range (IQR) values ​​instead of the mean, so that the cluster center points (centroids) remain stable and are not distorted by extreme data values. The research flow includes data collection of prospective Biznet Medan customers, data pre-processing (cleaning and scaling), feature selection (income, distance, and network requirements), and implementation of the K-Means model. Cluster quality evaluation is carried out by comparing the results of using StandardScaler and RobustScaler through the Silhouette Score and Davies Bouldin Index (DBI) metrics. The implementation successfully formed two main segments: Cluster 0 (Enterprise/Premium Customers) with high income characteristics and large bandwidth requirements (>200 Mbps), and Cluster 1 (Casual/Economical Customers) who dominate the market with lower network requirements (<50 Mbps). Optimization with Robust showed significant improvements in the evaluation metrics, where the Silhouette Score increased from 0.3133 to 0.6023, and the DBI value improved from 1.1421 to 0.8007. The implementation of Robust Scaling is effectively able to handle outlier problems in the K-Means algorithm, resulting in a more robust, efficient, and representative cluster structure. These results contribute to the processing of customer data for more objective strategic decision-making in ISP market expansion. Keywords: k-means; clustering; outlier; robust scaling; customer segmentation.   Abstrak: Pertumbuhan internet di Indonesia yang masif telah menciptakan persaingan kompetitif di antara penyedia jasa internet (ISP), termasuk Biznet Medan. Namun, karakteristik pelanggan yang sangat beragam dan bersifat heterogen membuat strategi pemasaran satu untuk semua (one-size-fits-all) menjadi tidak efektif dan tidak efisien. Data calon pelanggan yang besar sering ditemui data yang hilang, perbedaan nilai data yang ekstrim sehingga pengolahan data calon pelanggan untuk segmentasi sering kali terkendala oleh adanya data pencilan (outlier) yang dapat mengganggu akurasi pengelompokan pada algoritma konvensional. Penelitian ini bertujuan untuk mengoptimasi algoritma K-Means dalam melakukan segmentasi target pasar Biznet Medan agar tetap akurat meskipun terdapat data outlier, sehingga perusahaan dapat merancang strategi pemasaran yang lebih tepat sasaran. Untuk mengatasi sensitivitas K-Means terhadap pencilan, penelitian ini menerapkan teknik Robust Scaling. Solusi ini menggunakan nilai Median dan Interquartile Range (IQR) sebagai pengganti rata-rata, sehingga titik pusat klaster (centroid) tetap stabil dan tidak terdistorsi oleh nilai data yang ekstrem. Alur penelitian meliputi pengumpulan data calon pelanggan Biznet Medan, pra-pemrosesan data (pembersihan dan scaling), pemilihan fitur (penghasilan, jarak, dan kebutuhan jaringan), serta implementasi model K-Means. Evaluasi kualitas klaster dilakukan dengan membandingkan hasil penggunaan StandardScaler dan RobustScaler melalui metrik Silhouette Score dan Davies Bouldin Index (DBI). Implementasi berhasil membentuk dua segmen utama: Cluster 0 (Pelanggan Enterprise/Premium) dengan karakteristik penghasilan tinggi dan kebutuhan bandwidth besar (>200 Mbps), serta Cluster 1 (Pelanggan Casual/Hemat) yang mendominasi pasar dengan kebutuhan jaringan lebih rendah (<50 Mbps). Optimasi dengan Robust menunjukkan peningkatan signifikan pada metrik evaluasi, di mana Silhouette Score meningkat dari 0,3133 menjadi 0,6023, dan nilai DBI membaik dari 1,1421 menjadi 0,8007. Penerapan Robust Scaling secara efektif mampu menangani masalah outlier pada algoritma K-Means, menghasilkan struktur klaster yang lebih kuat, efisien, dan representatif. Hasil ini memberikan kontribusi pada pengolahan data pelanggan untuk pengambilan keputusan strategis yang lebih objektif dalam ekspansi pasar ISP. Kata kunci: K-Means; klasterisasi; data pencilan; Robust Scaling; Segmentasi Pelanggan.

The rapid development of information technology has encouraged schools to implement Academic Information Systems (AIS) to improve the effectiveness of academic data management. However, the quality of information system services is often not optimal, resulting in a gap between users' expectations and perceptions. This study aims to determine the priority areas for improving the quality of the Academic Information System at SMA Negeri 1 Talamau through the integration of the Analytical Hierarchy Process (AHP) and Importance Performance Analysis (IPA) methods. The AHP method is employed to determine the priority weights of each system quality criterion, while IPA is used to identify attributes that require improvement based on their importance and performance levels. The study adopts a quantitative approach involving students, teachers, and administrative staff as respondents. The results indicate that the information quality dimension has the highest priority weight, followed by system quality and service quality. The IPA analysis reveals that several attributes fall into Quadrant I (Concentrate Here), making them the main priorities for improvement, particularly regarding system access speed, academic information accuracy, and service responsiveness. This study produces a priority model that can be utilized by the school as a basis for decision-making in enhancing the quality of its Academic Information System.  

This study aims to determine the extent of the influence of work participation, work stress, and compensation on the employee performance of PT. Tidar Kerinci Agung Solok Selatan. The study consists of three independent variables, namely work participation, work stress, and compensation, and one dependent variable, employee performance. The analytical methods employed were correlation analysis and multiple regression analysis. Data were collected through questionnaires completed by employees of PT. Tidar Kerinci Agung Solok Selatan. Furthermore, based on the simultaneous hypothesis test (F-test), the calculated F-value was 19.077, which is greater than the F-table value of 2.76 (19.077 > 2.76), with a significance level of 0.000 (< 0.050). Therefore, it can be concluded that work participation, work stress, and compensation simultaneously have a positive and significant effect on employee performance. In addition, the coefficient of determination (R²) was found to be 0.467, or 46.7%. This indicates that 46.7% of the variation in employee performance can be explained by work participation, work stress, and compensation, while the remaining 53.3% is influenced by other variables not examined in this study.

The study aims to determine whether Guided Discovery Learning influences students' mathematical problem-solving ability assisted by the PhET application at VIIth-grade students of MTs Muhammadiyah 17 Punggulan. This study used a quantitative method with a Quasi-Experimental method using a Two-Group Pretest-Posttest Design. In addition, this study found that the sample used was normally distributed and homogeneous. The results of data analysis indicate that the instrument used was valid and reliable. The population in this study are VIIth-grade students of MTs Muhammadiyah 17 Punggulan, with a sample consisting of two classes: VIIth- 1 grade as the experimental class and VIIth- 2 grade as the control class. The instrument used was a posttest of mathematical problem solving ability using a t-test with a significance level of 0.05. The results of the t-test obtained tcount = 2.716> ttable = 2.004 so that H0 was rejected and H1 was accepted, so it can be concluded that there is an influence of the Discovery Learning learning model assisted by the PhET application on students' mathematical problem solving abilities on the material of flat shapes at VIIth grade studentas of  MTs Muhammadiyah 17 Punggulan in 2025/2026 Academic Year.

The reseach was conducted in Panca Arga Village, Rawang Panca Arga District, Asahan, North Sumatra, to determine the effect of Trichoderma sp. and Paenibacillus polymexa induction on rice blast and kresek disease resistance. The study used a factorial randomized block design with two factors: four levels of Trichoderma sp. concentration: T0 0 ml/liter, T1 25 ml/liter, T2 50 ml/liter, T3 70 ml/liter, and three levels of Paenibacillus polymexa bacteria: P0 0 ml/liter, P1 50 ml/liter, P2 100 ml/liter, with three replications. Observation parameters included plant height, number of tillers, flowering age, intensity of blast disease attacks, intensity of kresek disease attacks, harvest age, production per clump, and production per plot. The results showed that Trichoderma sp induction gave the best results at a plant height of 90.80 cm, the number of tillers was 30.58 plants, flowering age was 46.08 days, blast attack intensity was 0.51%, kresek attack intensity was 3.63%, harvest age was 110.72 days, production per clump was 64.78 g and production per plot was 1.61 kg. Paenibacillus polymyxa induction gave the best results at a plant height of 85.88 cm, the number of tillers was 30.15 plants, flowering age was 45.81 days, blast attack intensity was 4.20%, kresek attack intensity was 4.40%, harvest age was 110.31 days, production per clump was 61.25 g and production per plot was 1.54 kg. The interaction between Trichoderma sp and Paenibacillus polymyxa induction had no significant effect on plant height, number of tillers, flowering age, harvest age, production per clump, and production per plot. The interaction had a significant effect on the intensity of blast and kresek attacks

The Crime of Human Trafficking is a transnational crime that violates human rights through various forms of exploitation, including sexual exploitation, forced labor, and modern slavery. Although Indonesia has enacted Law Number 21 of 2007 on the Eradication of Human Trafficking, its enforcement still faces numerous challenges, including weak supervision, inadequate victim protection, and the emergence of digital-based trafficking methods. This study aims to analyze the weaknesses of the existing criminal law policy and to formulate a reformed legal policy by positioning human trafficking as an extraordinary crime. Using a normative legal research method, the findings indicate that human trafficking possesses the characteristics of an organized, transnational, and profit-driven crime, thereby requiring special legal treatment. Therefore, it is necessary to strengthen criminal sanctions, optimize asset confiscation, expand corporate criminal liability, and enhance victim protection and recovery mechanisms in order to achieve a more effective and just eradication of human trafficking.

Extreme weather in coastal areas such as Kota Padang is influenced by complex topography and local atmospheric dynamics, making daily rainfall prediction a critical challenge for disaster mitigation and sectoral planning. This study aims to develop a daily rainfall prediction model using a Deep Neural Network (DNN) and evaluate its performance. The dataset consists of daily temperature, humidity, and air pressure data collected from the Automatic Weather Station (AWS) of BMKG Maritime Teluk Bayur from January 2020 to December 2024, comprising 1,823 samples after preprocessing. The research methodology follows the CRISP-DM framework, which includes six main phases. The proposed DNN architecture contains three hidden layers (128, 64, and 32 neurons) with ReLU activation and dropout regularization, enhanced by lag and rolling average features to capture temporal patterns. The model achieved an MAE of 1.79, RMSE of 17.31, and R² of 0.8972, indicating strong predictive performance. The model was deployed through a Streamlit-based interactive dashboard

Problems in the new student admission process at SMK Negeri 1 Painan are still encountered, one of which is the administrative process that is carried out manually. This causes applicant data management to be less effective, prone to errors, and time-consuming in the selection process up to the announcement stage. In addition, the limitations of the manual system make it difficult for the admission committee to verify documents and prepare reports. Therefore, a web-based information system is needed to manage the processes of registration, selection, journaling, announcement, and re-registration in a more structured, efficient, and accessible manner. The research method used was the Iterative Development method, which is carried out gradually and repeatedly. The research stages consisted of requirements analysis to identify system needs, design to create the interface and workflow, and development to implement the main features such as dashboard, account management, registration form, document verification, announcement of selection results, and reporting.   The results of this study show that system testing using the White Box method obtained an average score of 89.27% in the very valid category. Meanwhile, testing using the Black Box method obtained an average score of 94.86% in the very valid category. The practicality test based on the responses of administrators/admission committees reached 97.22% in the very practical category, while user responses (prospective students) reached 94.5% in the very practical category. The conclusion of this study is that the web-based new student admission system developed with the Laravel framework has been tested valid and practical based on white box and black box testing, as well as user evaluations. Thus, the system is feasible to be used as the main tool in the admission process at SMK Negeri 1 Painan because it improves the effectiveness and efficiency of school administration.