Abstract:Abstract: The election of the Student Council President is an important process in building a democratic, responsible, and integrity-based leadership culture in the school environment. In addition, through the main discussion…
ssion in this study aims to optimize the selection process for candidates for Student Council President at SMP Negeri 11 Tanjung Balai through the implementation of a Decision Support System (DSS) based on the MOORA (Multi-Objective Optimization on the Basis of Ratio Analysis) method. The MOORA method is used to normalize values, weight criteria, and calculate optimization values to produce the best alternative ranking. Data were obtained through observation, interviews with school officials, and literature studies. The results of the study indicate that the MOORA method is able to provide objective and transparent ranking results, where M. Asril Sitorus Pane obtained the highest score of 0.347 (Rank 1), followed by M. Rian with a score of 0.333 (Rank 2). The implementation of this system is expected to be able to provide recommendations for the best candidates regularly and accurately, so that it can support fairer and more accountable decision-making in the next Student Council President election.
Keywords: decision support system; osis chairman election; MOORA; SMP negeri 11 tanjung balai.
Abstrak: Pemilihan Ketua OSIS merupakan proses penting dalam membangun budaya kepemimpinan yang demokratis, bertanggung jawab dan berintegritas di lingkungan sekolah. Selain itu, melalui pokok pembahasan pada penelitian ini bertujuan untuk dapat mengoptimalkan proses seleksi calon Ketua OSIS di SMP Negeri 11 Tanjung Balai melalui penerapan Sistem Pendukung Keputusan berbasis metode MOORA (Multi-Objective Optimization on the Basis of Ratio Analysis). Metode MOORA digunakan agar dapat melakukan normalisasi nilai, pembobotan kriteria serta perhitungan nilai optimasi guna menghasilkan peringkat alternatif terbaik. Data diperoleh melalui observasi, wawancara dengan pihak sekolah serta dan studi kepustakaan. Hasil penelitian menunjukkan bahwa metode MOORA mampu memberikan hasil peringkat yang objektif dan transparan, yang dimana M. Asril Sitorus Pane memperoleh nilai tertinggi sebesar 0,347 (Peringkat 1), diikuti oleh M. Rian dengan nilai 0,333 (Peringkat 2). Penerapan sistem ini diharapkan mampu memberikan rekomendasi kandidat terbaik secara sistematis dan akurat, sehingga dapat mendukung pengambilan keputusan yang lebih adil dan akuntabel dalam pemilihan Ketua OSIS selanjutnya.
Kata kunci: MOORA; pemilihan ketua osis; sistem pendukung keputusan; SMP negeri 11 tanjung balai.
Abstract:Abstract: Tech Kios Laptop Kisaran is a business engaged in selling used laptops with various brands and specifications to meet customer needs. However, the selection process is still conducted manually and relies on subjective…
jective judgment, which may result in less accurate recommendations. This study aims to design and implement a Decision Support System using the MOORA (Multi-Objective Optimization on the Basis of Ratio Analysis) method to objectively determine the best used laptop. The criteria applied in this study include brand, screen resolution, laptop size, and battery durability. The system was developed through requirement analysis, system design, implementation, and black-box testing. The results show that the system successfully generates rankings based on MOORA preference values. The highest optimization value of 0.4321 was achieved by Lenovo IdeaPad Slim (A04) and Lenovo ThinkPad (A06), indicating that these two alternatives are the best recommended used laptops. Therefore, the developed system enhances the objectivity, effectiveness, and accuracy of the laptop selection process at Tech Kios Laptop Kisaran.
Keywords: decision support system; MOORA; multi criteria; used laptop; recommendation.
Abstrak: Tech Kios Laptop Kisaran merupakan usaha yang bergerak di bidang penjualan laptop bekas dengan berbagai merek dan spesifikasi untuk memenuhi kebutuhan pelanggan. Namun, proses pemilihan laptop masih dilakukan secara manual dan bergantung pada penilaian subjektif, sehingga berpotensi menghasilkan rekomendasi yang kurang akurat. Penelitian ini bertujuan untuk merancang dan mengimplementasikan Sistem Pendukung Keputusan menggunakan metode MOORA (Multi-Objective Optimization on the Basis of Ratio Analysis) guna menentukan laptop bekas terbaik secara objektif. Kriteria yang digunakan dalam penelitian ini meliputi merek, resolusi layar, ukuran laptop, dan ketahanan daya baterai. Pengembangan sistem dilakukan melalui tahapan analisis kebutuhan, perancangan sistem, implementasi, serta pengujian menggunakan metode black-box. Hasil penelitian menunjukkan bahwa sistem mampu menghasilkan peringkat alternatif berdasarkan nilai preferensi MOORA. Nilai optimasi tertinggi sebesar 0,4321 diperoleh oleh Lenovo IdeaPad Slim (A04) dan Lenovo ThinkPad (A06), yang menunjukkan bahwa kedua alternatif tersebut merupakan rekomendasi laptop bekas terbaik. Dengan demikian, sistem yang dikembangkan mampu meningkatkan objektivitas, efektivitas, dan ketepatan dalam proses pemilihan laptop bekas di Tech Kios Laptop Kisaran.
Kata kunci: laptop bekas; MOORA; multi-kriteria; rekomendasi; sistem pendukung keputusan.
Abstract:Abstract: Rehabilitation programs are essential in correctional systems to equip inmates with the skills and behavioral readiness required for social reintegration. However, rehabilitation program assignment in many correctional…
ectional institutions remains dependent on manual and subjective assessments, which may result in inconsistent decisions. This study develops a Random Forest–based prediction system to support objective and data-driven rehabilitation program determination. A quantitative approach was applied using historical inmate data from January 2023 to January 2025, comprising 2,023 records. The research process included data preprocessing, an 80:20 training–testing split, model training, and performance evaluation using accuracy, precision, recall, and F1-score metrics. The results show that the model achieved an accuracy of 86.17% during training in Google Colab and 68.83% when deployed within the application system. This performance gap reflects real-world deployment and computational constraints rather than model failure. The proposed system provides consistent and objective rehabilitation program recommendations, thereby supporting more effective rehabilitation planning and decision-making in correctional institutions.
Keywords: correctional institutions; inmate rehabilitation programs; machine learning; random Forest; prediction system
Abstrak: Program pembinaan narapidana memiliki peran penting dalam sistem pemasyarakatan untuk membekali warga binaan dengan keterampilan serta kesiapan perilaku dalam proses reintegrasi ke masyarakat. Namun, pada banyak lembaga pemasyarakatan, penentuan program pembinaan masih bergantung pada penilaian manual yang bersifat subjektif, sehingga berpotensi menimbulkan ketidakkonsistenan dalam pengambilan keputusan. Penelitian ini mengembangkan sistem prediksi program pembinaan narapidana berbasis algoritma Random Forest guna mendukung pengambilan keputusan yang objektif dan berbasis data. Pendekatan kuantitatif diterapkan menggunakan data historis narapidana periode Januari 2023 hingga Januari 2025 sebanyak 2.023 data. Tahapan penelitian meliputi prapemrosesan data, pembagian data latih dan uji dengan rasio 80:20, pelatihan model, serta evaluasi performa menggunakan metrik akurasi, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa model mencapai akurasi sebesar 86,17% pada tahap pelatihan di Google Colab dan 68,83% saat diimplementasikan pada sistem aplikasi. Perbedaan performa tersebut mencerminkan keterbatasan lingkungan operasional, bukan kegagalan model. Secara keseluruhan, sistem yang dikembangkan mampu memberikan rekomendasi program pembinaan yang lebih objektif dan konsisten, sehingga mendukung perencanaan pembinaan yang lebih efektif.
Kata kunci: mesin pembelajaran; program pembinaan narapidana; random Forest; sistem pemasyarakatan; sistem prediksi
Abstract:Abstract: Academic achievement mapping is an important process in higher education to support effective academic monitoring and guidance. In practice, student grouping is often conducted manually by academic staff using…
simple criteria such as Grade Point Average (GPA) thresholds and subjective judgment, without systematic data analysis. This study aims to apply the Fuzzy C-Means (FCM) clustering algorithm to objectively group students based on their academic achievement levels. The dataset consists of academic records from 179 sixth-semester students of the Computer Science Study Program at Universitas Islam Negeri Sumatera Utara, where 160 eligible students are processed in the FCM calculation. Three variables are used: cumulative GPA, total completed credits, and the total number of low grades (D/E). The FCM algorithm automatically performs the mapping and groups students into three categories, namely excellent, stable, and at-risk students. Cluster quality is evaluated using the Silhouette Score and Davies–Bouldin Index, showing satisfactory clustering performance. The results indicate that the proposed approach provides a data-driven and objective basis for academic decision support.
Keywords: academic achievement; clustering; fuzzy c-means; student
Abstrak: Pemetaan pencapaian akademik mahasiswa merupakan proses penting dalam pendidikan tinggi untuk mendukung pemantauan dan pembinaan akademik yang tepat sasaran. Dalam praktiknya, pengelompokan mahasiswa masih sering dilakukan secara manual oleh pihak akademik berdasarkan kriteria sederhana, seperti batasan Indeks Prestasi Kumulatif (IPK) dan penilaian subjektif, tanpa analisis data yang sistematis. Penelitian ini bertujuan menerapkan algoritma Fuzzy C-Means (FCM) untuk mengelompokkan mahasiswa secara objektif berdasarkan tingkat pencapaian akademik. Data penelitian berasal dari 179 mahasiswa semester enam Program Studi Ilmu Komputer Universitas Islam Negeri Sumatera Utara, dengan 160 mahasiswa memenuhi kriteria dan diproses menggunakan algoritma FCM. Variabel yang digunakan meliputi IPK kumulatif, jumlah SKS yang telah ditempuh, dan total nilai rendah (D/E). Proses pemetaan sepenuhnya dilakukan oleh algoritma FCM dan menghasilkan tiga kategori mahasiswa, yaitu unggul, stabil, dan berisiko. Evaluasi menggunakan Silhouette Score dan Davies–Bouldin Index menunjukkan kualitas pengelompokan yang cukup baik.
Kata kunci: fuzzy c-means; clustering; mahasiswa; pencapaian akademik
Abstract:This study aims to apply the Analytic Network Process (ANP) method as a decision support tool in determining the eligibility of education grant recipients in North Sumatra Province. The background of this research arises…
from the large number of grant applicants compared to the available budget, as well as the absence of clear and objective evaluation standards. The ANP method was chosen because it allows the interdependence between assessment criteria such as institutional feasibility, performance and achievement, social and educational impact, and accountability and transparency to be analyzed comprehensively. Data were obtained through interviews, documentation, and observation at the North Sumatra Provincial Education Office. The results of the ANP model show that the criterion with the highest weight is accountability and transparency (0.44), followed by social and educational impact (0.31). Among the three alternatives, community-based education foundations (A2) obtained the highest total weight (0.30), indicating that they are the most eligible recipients of education grants. The implementation of the ANP-based decision support system produces valid and consistent ranking results (CR < 0.1), enabling faster, fairer, and more transparent decision-making. Therefore, the ANP method contributes significantly to improving governance, objectivity, and accountability in the distribution of education grants in North Sumatra Province.
Abstract:Abstract: Understanding students’ emotional conditions is important for evaluating engagement and learning atmosphere in classroom environments. However, conventional evaluation methods are often subjective and difficult…
lt to apply in real time. Therefore, this study proposes a real-time multi-face emotion detection system designed for classroom learning environments. The system integrates a CNN-based Tiny Face Detector for multi-scale face localization with a convolutional neural network to classify seven facial emotions: angry, disgust, fear, happy, sad, surprise, and neutral. Experimental evaluation was conducted using classroom video data under varying lighting conditions, face orientations, partial occlusions, and different numbers of detected faces per frame. The proposed system achieves stable real-time performance with processing speeds ranging from 10–20 FPS, depending on face density. The results show higher recognition performance for expressive emotions, while subtle emotions remain more challenging. Overall classification accuracy reaches above 80% when emotion predictions are aggregated across multiple faces and time windows. These results indicate that the proposed system is suitable for objective analysis of emotional dynamics in classroom environments and supports the deployment of lightweight emotion-aware monitoring systems for educational applications.
Keywords: classroom monitoring; convolutional neural network; facial emotion recognition; multi-face detection; tiny face detector.
Abstrak: Pemahaman terhadap kondisi emosional mahasiswa penting untuk mengevaluasi keterlibatan dan suasana pembelajaran di kelas. Namun, metode evaluasi konvensional umumnya bersifat subjektif dan sulit diterapkan secara real-time. Oleh karena itu, penelitian ini mengusulkan sistem deteksi emosi multi-wajah secara real-time yang dirancang untuk lingkungan pembelajaran di kelas. Sistem mengintegrasikan Tiny Face Detector berbasis CNN untuk pelokalan wajah multi-skala dengan jaringan saraf konvolusional untuk mengklasifikasikan tujuh emosi wajah, yaitu marah, jijik, takut, senang, sedih, terkejut, dan netral. Evaluasi eksperimen dilakukan menggunakan data video kelas dengan variasi kondisi pencahayaan, orientasi wajah, oklusi parsial, serta jumlah wajah yang berbeda dalam satu frame. Sistem menunjukkan kinerja real-time yang stabil dengan kecepatan pemrosesan antara 10–20 FPS, bergantung pada kepadatan wajah. Hasil pengujian menunjukkan kinerja yang lebih baik pada emosi ekspresif, sementara emosi dengan ciri halus lebih menantang untuk dikenali. Akurasi klasifikasi keseluruhan mencapai di atas 80% ketika hasil emosi diagregasi berdasarkan banyak wajah dan interval waktu. Hasil ini menunjukkan bahwa sistem yang diusulkan berpotensi digunakan untuk analisis objektif dinamika emosi di kelas serta mendukung pemantauan lingkungan pembelajaran berbasis kecerdasan buatan.
Kata kunci: pengenalan emosi wajah; deteksi multi-wajah; Tiny Face Detector; jaringan saraf konvolusional; pemantauan kelas.
Abstract:Abstract: The implementation of dress code regulations in university environments is generally still carried out conventionally, requiring significant time and effort and potentially leading to subjective assessments. This…
is study develops an automatic student dress code compliance detection system using computer vision based on the YOLOv8 model. The dataset consists of 1,800 annotated images divided into eight clothing categories, split into 78% training (1,404 images), 14% validation (254 images), and 8% testing (143 images). All images underwent preprocessing and data augmentation before training the YOLOv8 model with an input size of 640×640 pixels for 50 epochs. During testing, the YOLOv8 model achieved an overall performance of Precision 0.844, Recall 0.773, F1-Score 0.802, and mAP@0.5 0.841, and was able to detect clothing objects with good accuracy and stable performance under various image conditions. The system was integrated with a Flask-based backend and a web-based frontend to enable real time detection and compliance classification, with a response time of less than 2 seconds, supporting automatic and consistent identification of student dress code compliance as “Compliant” or “Violation.”
Keywords: compliance detection; computer vision; dress code regulations; real time detection; YOLOv8.
Abstrak: Penerapan aturan berpakaian di lingkungan kampus umumnya masih dilakukan secara konvensional sehingga membutuhkan waktu dan tenaga yang relatif besar serta berpotensi menimbulkan subjektivitas penilaian. Penelitian ini bertujuan mengembangkan sistem pendeteksi kepatuhan berpakaian mahasiswa secara otomatis berbasis visi komputer menggunakan model YOLOv8. Dataset yang digunakan terdiri dari 1.800 citra beranotasi yang terbagi ke dalam 8 kategori pakaian, dengan pembagian data sebesar 78% data latih (1.404 citra), 14% data validasi (254 citra) dan 8% data uji (143 citra). Seluruh citra diproses melalui tahapan pre-processing dan data augmentation, kemudian digunakan untuk melatih model YOLOv8 dengan ukuran input 640×640 piksel selama 50 epoch. Pada tahap pengujian, model mencapai performa keseluruhan dengan Precision 0.844, Recall 0.773, F1-Score 0.802, dan mAP@0.5 0.841, serta mampu mendeteksi objek pakaian dengan akurasi baik dan performa stabil pada berbagai kondisi citra. Sistem kemudian diintegrasikan dengan backend berbasis Flask dan frontend web untuk mendukung proses deteksi waktu nyata dan klasifikasi kepatuhan, dengan waktu respons sistem kurang dari 2 detik, sehingga mampu mengidentifikasi status kepatuhan berpakaian mahasiswa ke dalam kategori “Aman” dan “Melanggar Aturan” secara otomatis dan konsisten.
Kata kunci: aturan berpakaian; deteksi waktu nyata; pendeteksi kepatuhan; visi komputer; YOLOv8.
Abstract:Abstract: The determination of thesis topics is a fundamental stage in academic research, yet the evaluation process remains predominantly manual and subjective. This reliance on individual lecturer perception often leads…
s to inconsistent feasibility assessments and fails to systematically measure the topic's alignment with strategic needs. This research aims to develop a Decision Support System (DSS) model based on fuzzy logic to assess the feasibility of thesis topics objectively and systematically, focusing on enhancing the relevance of student research. The research method employed the Fuzzy Inference System (FIS) with the Sugeno method. This model was designed through literature review and FGD to establish four criteria (Topic Relevance, Difficulty Level, Idea Novelty, Reference Availability) and 81 rule bases. The model validation results against expert judgment using 15 test data showed a high accuracy rate of 91.31%, with a Mean Absolute Percentage Error (MAPE) value of 8.69%. In conclusion, this DSS model is proven to be valid and consistent, and it can be relied upon as an objective tool to improve the quality and relevance of thesis topics.
Keywords: academic evaluation; decision support system; fuzzy logic; fuzzy sugeno; thesis feasibility
Abstrak: Penentuan topik skripsi merupakan tahapan fundamental dalam penelitian akademik, namun proses evaluasinya hingga kini masih cenderung manual dan subjektif. Ketergantungan pada persepsi dosen secara individu sering kali menyebabkan penilaian kelayakan yang tidak konsisten serta kegagalan dalam mengukur keselarasan topik dengan kebutuhan strategis secara sistematis. Penelitian ini bertujuan mengembangkan model Sistem Pendukung Keputusan (SPK) berbasis logika fuzzy untuk menilai kelayakan topik skripsi secara objektif dan sistematis, dengan fokus pada peningkatan relevansi penelitian mahasiswa. Metode penelitian yang digunakan adalah Fuzzy Inference System (FIS) dengan metode Sugeno. Model ini dirancang melalui tinjauan pustaka dan Focus Group Discussion (FGD) untuk menetapkan empat kriteria (Relevansi Topik, Tingkat Kesulitan, Kebaruan Ide, Ketersediaan Referensi) serta 81 basis aturan. Hasil validasi model terhadap penilaian pakar menggunakan 15 data uji menunjukkan tingkat akurasi yang tinggi yaitu 91,31%, dengan nilai Mean Absolute Percentage Error (MAPE) sebesar 8,69%. Kesimpulannya, model SPK ini terbukti valid dan konsisten, serta dapat diandalkan sebagai alat objektif untuk meningkatkan kualitas dan relevansi topik skripsi.
Kata kunci: evaluasi akademik; sistem pendukung keputusan; logika fuzzy; fuzzy sugeno; kelayakan skripsi
Abstract:Abstract: The development of digital learning systems requires not only effective content delivery but also database consistency and performance, particularly when used at scale by lecturers and students. Weaknesses in database…
atabase design can lead to data duplication, relational violations, and transaction failures that compromise system reliability. This study designed the Royal Mengajar application using PHP and MySQL, supported by JavaScript, HTML, and Bootstrap 5. The Crowdsourced Academic Content model enables lecturers to contribute learning materials openly, while students evaluate them through a user rating system. The objective of this research is to design and optimize the database architecture of the Royal Mengajar application by implementing multiple control mechanisms—namely views, triggers, transactions, and constraints—to enhance data efficiency, consistency, and integrity in digital learning environments. Database optimization focuses on the use of views to improve query efficiency, triggers to maintain automatic consistency, transactions to ensure atomicity in multi-table operations, and constraints to preserve data integrity. The results show that views reduced the average query execution time to 0.12 seconds, triggers maintained consistency without manual intervention, and constraints achieved 100% referential integrity. The application of these mechanisms significantly improved system speed, reduced data redundancy, and enhanced information reliability, thus reinforcing the sustainability of Royal Mengajar as a community-driven learning platform
Keywords: crowdsourced academic content; constraint; database optimization; trigger.
Abstrak: Pengembangan sistem pembelajaran digital tidak hanya menuntut penyajian materi, tetapi juga konsistensi serta kinerja basis data ketika sistem digunakan secara masif oleh dosen dan mahasiswa. Kelemahan rancangan database dapat menimbulkan duplikasi data, pelanggaran relasi, dan kegagalan transaksi yang memengaruhi keandalan sistem. Penelitian ini merancang aplikasi Royal Mengajar berbasis PHP dan MySQL dengan dukungan JavaScript, HTML, dan Bootstrap 5. Model Crowdsourced Academic Content memungkinkan dosen berkontribusi secara terbuka, sedangkan mahasiswa melakukan evaluasi melalui user rating system. Tujuan penelitian ini adalah untuk merancang dan mengoptimalkan basis data aplikasi Royal Mengajar melalui penerapan berbagai mekanisme pengendali, seperti view, trigger, transaction, dan constraint, guna meningkatkan efisiensi, konsistensi, dan integritas data dalam sistem pembelajaran digital. Optimalisasi database difokuskan pada penerapan view untuk efisiensi query, trigger untuk menjaga konsistensi otomatis, transaction untuk memastikan atomicity pada operasi multi-tabel, serta constraint guna menjamin integritas data. Hasil pengujian menunjukkan view menurunkan rata-rata waktu eksekusi query menjadi 0,12 detik, trigger menjaga konsistensi tanpa intervensi manual, dan constraint memastikan integritas referensial tercapai 100%. Penerapan mekanisme ini berdampak pada peningkatan kecepatan sistem, berkurangnya redundansi, serta keandalan informasi yang lebih tinggi, sehingga mendukung keberlanjutan Royal Mengajar sebagai platform pembelajaran berbasis kontribusi komunitas.
Kata kunci: basis data; optimasi; trigger; constraint; crowdsourced academic content.
Abstract:Abstract: YouTube is one of the most popular video streaming platforms, but it has constraints that can cause problems when clients have difficulty finding content according to their wishes. The main objective of this study…
udy is to increase user capacity in viewing content specifically in the field of women's empowerment. By using content-based filtering techniques, the system will analyze user preferences and interests through recommendations for women's empowerment content. The data source is via the YouTube API and is analyzed using PHP programming content-based filtering techniques. The system's recommendations provide a list of women's empowerment content with a user request display. The results of the research evaluation obtained a precision value of 62%, meaning that the recommendations match the topic being searched for, namely women's empowerment. The recall value of 84% indicates that the system has succeeded in finding relations from the database. The f1-score value of 72% indicates that there is a balance between precision and recall, meaning that a system is needed that is not only accurate but also complete. While the cosine value shows a score of 0.7071 approaching the maximum value (1.0). The recommendation of the content-based filtering method produces quite effective women's empowerment content.
Keywords: content-based filtering, recommendations, women Empowerment, youtube