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All Journal JOIV : International Journal on Informatics Visualization PREPOTIF : Jurnal Kesehatan Masyarakat Indonesian Journal Computer Science (ijcs) Journal of Elektronik Sistem InformasI International Journal of Applied Information Systems and Informatics
Genta Nazwar Tarempa
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Computer Science & IT Decision Sciences, Operations Research & Management Engineering Public Health

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PENGEMBANGAN BUKU SAKU ELEKTRONIK TENTANG POLA MAKAN SEHAT BAGI KEHIDUPAN MAHASISWA KOST Nugrahaeni, Meita Tyas; Yutanti, Wulan Tri; Tarempa, Genta Nazwar; Annashr, Nissa Noor
PREPOTIF : JURNAL KESEHATAN MASYARAKAT Vol. 9 No. 2 (2025): AGUSTUS 2025
Publisher : Universitas Pahlawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/prepotif.v9i2.48948

Abstract

Pola makan yang tidak sehat menjadi salah satu faktor risiko utama berbagai penyakit tidak menular (PTM), khususnya pada mahasiswa yang tinggal di indekos. Permasalahan kesehatan yang sering terjadi akibat pola makan tidak sehat diantaranya Penyakit Tidak Menular seperti paru obstruk kronik, diabetes mellitus, overweight dan obesitas, hipertensi, kolesterol, dan kardiovaskular, serta kesehatan mental seperti stress, depresi, dan gangguan kecemasan yang berakibat pada gangguan pada perilaku makan. Penelitian ini bertujuan mengembangkan media promosi kesehatan berupa buku saku elektronik untuk meningkatkan pengetahuan mengenai pola makan sehat. Penelitian menggunakan pendekatan research and development dengan tahapan P-Process, meliputi analisis situasi, desain strategis, pengembangan dan uji coba media, implementasi dan monitoring, serta evaluasi. Subjek penelitian adalah 25 mahasiswa yang tinggal di Kost Ladies Accomodation, Kota Tasikmalaya. Teknik pengambilan sampel menggunakan total sampling dengan metode pengumpulan data kuantitatif (kuesioner pre- test dan post- test) serta kualitatif (wawancara mendalam). Evaluasi efektivitas media dilakukan melalui uji Wilcoxon. Hasil menunjukkan peningkatan signifikan pengetahuan setelah intervensi, dengan nilai p- value sebesar 0,000, dengan rata- rata post- test (88,40) lebih tinggi dibanding pre- test (66,80). Media promosi kesehatan digital melalui buku saku elektronik “Sudahkah Kamu Makan Hari Ini?” terbukti efektif dalam meningkatkan pengetahuan dan berpotensi mendorong perubahan perilaku pola makan sehat di kalangan mahasiswa kost.
Implementation of Apriori Algorithm to Analyze Sales Transaction Patterns in Official E-Commerce Adam Nugraha, Yuki Rizki; Maftahuhillah, Alma Ariz; Nur Rachman, Andi; Fitriani, Euis Nur; Tarempa, Genta Nazwar
JESII: Journal of Elektronik Sistem InformasI Vol 3 No 1 (2025): JOURNAL ELEKTRONIK SISTEM INFORMASI (JUNE)
Publisher : Departement Information Systems Universitas Kebangsaan Republik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31848/jesii.v3i1.4127

Abstract

The growth of e-commerce in Indonesia has driven dynamic changes in consumer behavior, especially in the purchasing patterns of fashion products. However, most businesses have not optimally utilized transaction data to design targeted marketing strategies. One of the main challenges is the inability to systematically recognize customer purchase patterns from complex and large transaction data. This research aims to apply the Apriori algorithm, specifically the FP-Growth method, in identifying recurring purchase patterns based on product combinations that are often purchased together at the Qeela Official store, an e-commerce-based fashion business. The data used includes 20,000 transactions during the period January to April 2024, which were sampled into 10,000 transactions according to the RapidMiner system limitations. The research stages include data transformation to binary format, conversion of attributes to binominal, application of the FP-Growth algorithm, and formation of association rules using minimum support parameters of 0.001 and minimum confidence of 0.5. The results show the existence of strong association patterns, such as SHORT PARASUT → SHORT CARGO with a confidence of 82.1% and a lift of 3.197. The insights provide a strong basis for decision-making in product bundling strategies, cross-selling implementation, automated recommendations, and stock management. The data mining approach used is proven to be relevant and applicable to improve marketing effectiveness and operational efficiency in e-commerce businesses, especially in the highly competitive fashion industry.
K-Nearest Neighbor and Weight K-Nearest Neighbor Classification of Cork Fish Using Gray-Level-Co-Occurrence Matrix Algorithm Approach Fitriani Dewi, Euis Nur; Rachman, Andi Nur; Nur Shofa, Rahmi; Tarempa, Genta Nazwar
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.2745

Abstract

Ornamental cork fish is a type of fish that is in great demand among the public as an ornamental fish. Ornamental cork fish have various types and colors; each variation has its own name and is a selling point among ornamental cork fish lovers. With a good motif, ornamental cork fish will have an expensive market value. However, for the most part, there are still many who do not know for sure what type of ornamental cork fish is included in the variation type classification because the colors are varied and seem similar. Because of this, this research created a system that can classify types of ornamental cork fish automatically based on data while still paying attention to the level of accuracy of the classification. The algorithm used for the initial classification process is KNN, which is chosen for its accuracy comparison level value. This algorithm does not consider the weight of each data point to be classified. The data processing process carried out only looks at the highest number of classes, which becomes the benchmark for labels from the classification results. In the classification process method using the KNN algorithm, there are still shortcomings in the classification process, so this research carried out a process of comparing classification accuracy using the Weight-KNN algorithm to increase the classification accuracy value. The process of the Weight-KNN algorithm stages is to carry out classification based on nearest neighbors first but still paying attention to the weight of each data. So that the classification process of determining the type of ornamental cork fish variation will be more accurate. Based on the results of experiments conducted, this research will focus on comparing the classification results between the KNN and Weight-KNN algorithms on ornamental cork fish. The results obtained state that the Weight-KNN algorithm has a higher level of accuracy with a weight of 83.6%, whereas using the KNN algorithm, it is only 80.6%.
Sistem Presensi Perkuliahan Mahasiswa Berbasis Face Recognition dengan Metode Liveness Detection Dewi, Euis Nur Fitriani; Tarempa, Genta Nazwar; Rachman , Andi Nur; Febriansyah, Andika
Indonesian Journal Computer Science Vol. 4 No. 2 (2025): Oktober 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/ijcs.v4i2.9876

Abstract

Sistem presensi merupakan komponen penting dalam institusi pendidikan untuk memastikan kehadiran mahasiswa tercatat secara efisien dan akurat. Metode presensi konvensional seperti tanda tangan manual, kartu identitas, atau QR code masih memiliki kelemahan, antara lain rawan kecurangan dan manipulasi data. Untuk mengatasi masalah tersebut, penelitian ini mengembangkan sistem presensi perkuliahan mahasiswa berbasis web yang mengintegrasikan YOLOv11 untuk deteksi wajah secara real-time, InsightFace untuk ekstraksi dan identifikasi fitur wajah, serta liveness detection sebagai mekanisme anti-spoofing. Sistem dibangun menggunakan framework Flask pada backend, ReactJS pada frontend, dan MySQL sebagai basis data. Hasil pengujian menunjukkan bahwa YOLOv11 mampu mendeteksi wajah dengan cepat dan akurat pada berbagai kondisi pencahayaan dengan tingkat precision 96,8%, recall 95,7%, akurasi keseluruhan 96,3% dan F1-Score sebesar 96,24%. Integrasi ketiga teknologi tersebut menghasilkan sistem presensi mahasiswa yang lebih efisien, akurat, dan aman dibandingkan metode konvensional. Penelitian ini memberikan kontribusi inovatif dalam meningkatkan keandalan serta keamanan proses presensi perkuliahan dengan memanfaatkan teknologi face recognition berbasis web. 
Application of Content-Based Filtering for Moisturizer Recommendation System Based on Skin Type Suitability Iswanto, Muhammad Edi; Latifah, Azzahra Putri; Rachman, Andi Nur; Tarempa, Genta Nazwar
Journal of Applied Information System and Informatic (JAISI) Vol 3, No 1 (2025): MEI 2025
Publisher : Deparment Information System, Siliwangi University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/jaisi.v3i1.15531

Abstract

Many users face significant challenges when trying to select the most suitable moisturizer for their skin. This difficulty often arises due to the overwhelming variety of available products on the market, combined with a lack of personalized information that could guide users toward the best choice. To address this issue, the present study aims to develop a recommendation system based on the Content-Based Filtering approach, which is specifically designed to align the benefits of moisturizer products with the unique needs of users' skin types. The data for this study were collected manually from 17 moisturizer products featured on the Sociolla e-commerce platform. Each product was carefully analyzed according to the descriptive information provided, including the benefits claimed and the skin types for which the product is recommended. The methodology involved several important steps: preprocessing the text from product descriptions, applying TF-IDF to assign term weights, and calculating cosine similarity scores between the user’s skin profile and product attributes. The analysis revealed that products such as D10 and D6, which yielded the highest similarity values, are strongly aligned with particular skin types. The resulting system demonstrates its ability to generate relevant and personalized product suggestions without the need for prior user preference data. This study concludes that using descriptive content as the foundation for recommendation logic can significantly enhance accuracy and targeting. Future enhancements may involve expanding the product database, integrating user-generated reviews, and leveraging machine learning techniques to produce even more adaptive and intelligent recommendations.
Implementation of Neural Collaborative Filtering for Social Aid Recipient Recommendation Febriyanto, Erick; Tarempa, Genta Nazwar; Dewi, Euis Nur Fitriani; Al-Husaini, Muhammad; Faishal, Rifda Tri
Journal of Applied Information System and Informatic (JAISI) Vol 3, No 2 (2025): November 2025
Publisher : Deparment Information System, Siliwangi University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/jaisi.v3i2.16944

Abstract

Social assistance needs system accurate recommendations for ensure distribution appropriate target. Research This aims to implement Neural Collaborative Filtering (NCF) to recommend recipient help social based on integration of dynamic parameters of poverty data. The NCF method was chosen Because his ability combines Generalized Matrix Factorization (GMF) and Multi-Layer Perceptron (MLP) to catch non-linear relationship between data. The dataset is taken from 845 recipients assistance in Cijulang Village, District Ciamis, with criteria covering employment, income, health, and family history assistance. The preprocessing stage includes data cleaning, label encoding, one-hot encoding, and data splitting (training-validation 80:20). The NCF architecture is built with embedding layer (dimension 32), hidden layer MLP (128-64-32 neurons), and output layer that combines GMF and MLP. Evaluation using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The results show that the model achieves RMSE 0.63 and MAE 0.47 on the training data, but overfitting occurred with a validation RMSE of 1.40 and MAE of 1.24. Analysis indicates the need for hyperparameter optimization (e.g., regulation, dropout rate) for an increase in generalization. Findings This prove NCF potential in increase accuracy recommendation help social, at the same time highlight importance data handling no balance and sparsity in context poverty. Implications study covers improvement transparency distribution assistance and reduction jealousy social through recommendation data -based. This study gives contribution methodological in NCF adaptation for sector public.
Co-Authors Adam Nugraha, Yuki Rizki Al-Husaini, Muhammad Annashr, Nissa Noor Dewi, Euis Nur Fitriani Erick Febriyanto Faishal, Rifda Tri Febriansyah, Andika Fitriani Dewi, Euis Nur Fitriani, Euis Nur Iswanto, Muhammad Edi Latifah, Azzahra Putri Maftahuhillah, Alma Ariz Nugrahaeni, Meita Tyas Nur Rachman, Andi Nur Shofa, Rahmi Rachman , Andi Nur Rachman, Andi Nur Yutanti, Wulan Tri