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All Journal ComEngApp : Computer Engineering and Applications Journal TEKNIK INFORMATIKA Scientific Journal of Informatics Jurnal Pengabdian Kepada Masyarakat (Indonesian Journal of Community Engagement) Produktif : Jurnal Ilmiah Pendidikan Teknologi Informasi JITK (Jurnal Ilmu Pengetahuan dan Komputer) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Applied Information System and Management Jurnal Sisfokom (Sistem Informasi dan Komputer) IJISTECH (International Journal Of Information System & Technology) Abdimas Umtas : Jurnal Pengabdian kepada Masyarakat Jurnal Pendidikan dan Konseling Indonesian Journal of Business Intelligence (IJUBI) ILKOMNIKA: Journal of Computer Science and Applied Informatics Jurnal Berdaya Mandiri Suluah Bendang: Jurnal Ilmiah Pengabdian Kepada Masyarakat Tematik : Jurnal Teknologi Informasi Komunikasi Innovation in Research of Informatics (INNOVATICS) IJISTECH Teknik: Jurnal Ilmu Teknik dan Informatika J-SAKTI (Jurnal Sains Komputer dan Informatika) TRIBUTE: JOURNAL OF COMMUNITY SERVICES Parta: Jurnal Pengabdian Kepada Masyarakat PAKDEMAS : Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Masyarakat As-salam Publikasi Hasil Pengabdian Kepada Masyarakat. Jurnal Karya Abdi Masyarakat Jurnal Pengabdian Pada Masyarakat Jurnal Pengabdian dan Pemberdayaan Masyarakat Indonesia Jurnal Relawan dan Pengabdian Masyarakat REDI TeknoKreatif: Jurnal Pengabdian kepada Masyarakat Inovasi Gagasan Abdimas & Kuliah Kerja Nyata JITEEHA: Journal of Information Technology Applications in Education, Economy, Health and Agriculture
Praseptiawan, Mugi
Teknik Informatika, Institut Teknologi Sumatera
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ANALISIS MODEL SISTEM REKOMENDASI KURSUS MOOC DENGAN METODE COLLABORATIVE FILTERING DAN INTEGRASI EXPLAINABLE AI Putri, Nabila Muthia; Praseptiawan, Mugi; Untoro, Meida Cahyo
Indonesian Journal of Business Intelligence (IJUBI) Vol 7 No 1 (2024): Indonesian Journal of Business Intelligence (IJUBI)
Publisher : Universitas Alma Ata

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21927/ijubi.v7i1.4274

Abstract

Sistem rekomendasi kursus Massive Open Online Course (MOOC) berperan penting dalam mendukung pembelajaran daring dengan memberikan saran kursus yang sesuai dengan preferensi pengguna. Dalam penelitian ini, kami mengembangkan model sistem rekomendasi kursus MOOC berbasis Collaborative Filtering dengan memanfaatkan dataset Coursera yang telah diproses. Preprocessing meliputi pembersihan data, penghapusan label yang tidak diperlukan, alokasi label, penghapusan data duplikat, dan analisis sentimen untuk memastikan konsistensi antara ulasan dan penilaian. Implementasi Collaborative Filtering melibatkan pembuatan tabel pivot, perhitungan Centered Cosine Similarity, dan prediksi penilaian kursus untuk pengguna yang belum pernah mengambil kursus tertentu. Evaluasi kinerja model dilakukan menggunakan metrik Root Mean Squared Error (RMSE) untuk mengukur tingkat kesalahan prediksi model. Hasil analisis dan evaluasi menunjukkan bahwa model yang dikembangkan berhasil memberikan rekomendasi kursus dengan tingkat kesalahan yang rendah, seperti yang tercermin dari nilai RMSE yang diperoleh yaitu 0.24 untuk sistem rekomendasi kursus MOOC. Integrasi Explainable AI dengan teknik LIME juga membantu dalam menjelaskan dan memahami rekomendasi yang diberikan oleh sistem, meningkatkan penjelasan tambahan terhadap model yang dibuat. Saran untuk pengembangan lebih lanjut termasuk fokus pada peningkatan interpretabilitas model dengan memperdalam integrasi Explainable AI, menggunakan dataset yang lebih besar, serta diversifikasi teknik pemodelan untuk meningkatkan kualitas dan akurasi rekomendasi yang diberikan oleh sistem.
Purchase Pattern Analysis on Komol Kopi Transaction Data Using Apriori Algorithm Pratama, Dafa Septian Putra; Praseptiawan, Mugi; Paramita, Niken
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 2 No. 3 (2025): October
Publisher : Lumina Infinity Academy Foundation

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This research aims to analyze purchasing patterns in Komol Kopi transaction data using the Apriori algorithm. This algorithm enables the discovery of relationships between items in large datasets that can be used to support business decisions, such as bundling promotions and inventory management. The dataset includes 12 transactions with various combinations of items, such as Kopi Hitam, Kopi Tubruk, and Nasi Telur. The analysis results show some significant purchase patterns with high support, confidence, and lift values. An example of an association found is between Kopi Hitam and Es Teh, which provides insights for more effective marketing strategies. This study confirms that the Apriori algorithm is an efficient tool in unearthing purchasing patterns, providing a solid foundation for the development of data-driven business strategies. Further research can integrate this analysis with recommendation systems to improve customer experience.
Supply Chain Optimization in the Retail Industry by Integrating Apriori Algorithms and Time Series Forecasting in Business Intelligence Putra, Gusty Nanda Kharisma; Silviana, Silviana; Riyadi, Agung; Praseptiawan, Mugi
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 3 No. 1 (2026): Vol. 3 No. 1 (2026): February
Publisher : Lumina Infinity Academy Foundation

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Abstract

This study investigates the integration of the Apriori algorithm and time series forecasting within a Business Intelligence (BI) framework to optimize supply chain operations in the retail industry. The Apriori algorithm was utilized to identify significant purchasing patterns, enabling strategic decisions such as product bundling and cross-selling. Concurrently, time series forecasting, with an ARIMA model achieving a mean absolute percentage error (MAPE) of 8%, provided accurate demand predictions, supporting improved inventory management and resource allocation. The integration of these methods into a BI dashboard facilitated real-time monitoring and data-driven decisionmaking, leading to enhanced operational efficiency and reduced costs. While challenges such as data quality, computational resource demands, and user adaptability were observed, this research underscores the transformative potential of analytics in retail supply chain management. Future advancements in machine learning and IoT integration are recommended to further enhance system performance. Overall, this study demonstrates a pathway for retailers to achieve operational excellence and superior customer satisfaction through data-driven strategies.
Analisis Sentimen Ulasan by.U dengan Pelabelan Rating dan Leksikon Menggunakan Multinomial Naïve Bayes Fatihanursari Dikananda; Bani Nurhakim; Dian Ade Kurnia; Ahmad Rifai; Mugi Praseptiawan
TEMATIK Vol. 13 No. 1 (2026): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Juni 2026
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/tematik.v13i1.2996

Abstract

Perkembangan layanan telekomunikasi digital mendorong bertambahnya jumlah ulasan pengguna yang digunakan sebagai bahan informasi guna mendukung pengambilan keputusan berbasis data. Penelitian ini bertujuan menganalisis sentimen ulasan aplikasi by.U menggunakan dua metode pelabelan data, yaitu rating-based labeling dan lexicon-based labeling, menggunakan algoritma Multinomial Naïve Bayes (MNB). Metode penelitian menerapkan framework Knowledge Discovery in Databases yang meliputi tahapan selection, preprocessing, transformation, data mining, dan evaluation. Dataset penelitian diperoleh dari Google Play sebanyak 8.000 ulasan berbahasa Indonesia. Tahap prapemrosesan mencakup cleaning, case folding, normalisasi, tokenisasi, stopword removal, serta stemming. Representasi fitur dilakukan menggunakan TF-IDF, sedangkan penyeimbangan data diterapkan melalui metode SMOTE. Evaluasi model dilakukan menggunakan metrik accuracy, precision, recall, dan F1-score dengan skema 10-fold cross validation. Hasil penelitian menunjukkan bahwa pendekatan lexicon-based labeling memberikan performa yang lebih baik dibandingkan rating-based labeling. Pendekatan rating-based menghasilkan accuracy sebesar 82,59%, precision 83,79%, recall 82,59%, dan F1-score 82,43%. Sementara itu, pendekatan lexicon-based memperoleh accuracy sebesar 88,96%, precision 89,69%, recall 88,96%, serta F1-score 88,91%. Temuan tersebut menunjukkan bahwa strategi pelabelan memiliki pengaruh terhadap performa klasifikasi sentimen. Pendekatan berbasis leksikon dinilai lebih efektif karena mampu memahami konteks linguistik dan ekspresi emosional pengguna secara lebih baik dibandingkan pendekatan berbasis rating.
Pemberdayaan Masyarakat Desa Cahaya Negeri Lampung Melalui Program Kuliah Kerja Nyata Mugi Praseptiawan; Afra Wafiqah Azhar
Jurnal Igakerta Vol. 1 No. 1 (2024): Jurnal Igakerta
Publisher : IGAKERTA Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70234/0xraa523

Abstract

Desa Cahaya Negeri yang terletak di Kecamatan Lemong, Kabupaten Pesisir Barat, Provinsi Lampung, mengalami berbagai permasalahan seperti keselamatan berlalu lintas yang rendah, kerusakan infrastruktur sekolah, serta luapan air yang berlebih. Kegiatan pengabdian ini bertujuan untuk meningkatkan pengetahuan masyarakat tentang keselamatan berlalu lintas, memperbaiki fasilitas pendidikan, dan mengatasi masalah drainase. Metode yang digunakan meliputi sosialisasi, perancangan desain, serta implementasi solusi infrastruktur. Hasil kegiatan menunjukkan peningkatan pengetahuan masyarakat tentang keselamatan berkendara, perbaikan fasilitas sekolah dasar, dan rancangan saluran drainase yang mampu mengurangi risiko banjir. Kesimpulannya, kegiatan ini berhasil memberikan dampak positif terhadap kualitas hidup masyarakat Desa Cahaya Negeri melalui pendidikan dan infrastruktur yang lebih baik.
Redesigning UI/UX of A Mobile Application Using Task Centered System Design Approach Mugi Praseptiawan; Meida Cahyo Untoro; Feri Fahrianto; Pungki Resti Prabandari; M. Syamsuddin Wisnubroto
Applied Information System and Management (AISM) Vol. 6 No. 1 (2023): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v6i1.24665

Abstract

Digital transformation requires a software system development method to identify and analyze user needs. In this research, software system development uses the Task Centered System Design framework with several stages, including identification, needs analysis, design, and evaluation. The identification stage is carried out by conducting interviews with stakeholders, and then the results of the interviews are analyzed and approved by stakeholders. This study aims to obtain user needs to build an application interface by applying the steps of the Task Centered System Design method and usability evaluation and calculating the weight of the feasibility value by testing the Heuristics method and System Usability Scale on the solution application design. The evaluation phase aims to determine the value of the usability problem in the design that has been designed. The evaluation phase uses the Usability Heuristic method by involving experts in the field of software development and the System Usability Scale method involving end users. After conducting research from the identification to the evaluation stage, the average severity rating of the Heuristic Usability test component scored less than 1 (one) in the second iteration, and the System Usability Scale results scored 70.3 for admin and 73.75 for the customer application. This result is in grade C with an adjective rating of Good.  
Co-Authors Abillah, Bintang Adinda Sekar Tanjung Aditya Wahyu Nugraha Afra Wafiqah Azhar Afriansyah, Aidil Agung Riyadi Ahmad Naim Bin Che Pee Ahmad Rifai Aidil Afriansya Aidil Afriansyah Aidil Afriasnyah Alam Fathurochman, Alam Alfajar Puja Kusuma Algifari, Muhammad Habib Amirul Iqbal Amrulloh, Iqbal Andika Setiawa Andika Setiawan Andre Febrianto Anggraini , Ade Eka Ardi Gaya Manalu Arre Pangestu Athalla, Muhammad Nadhif Bahri, Samsu Bani Nurhakim Baraku, Randi Baskara, Rizandi Agung Dadan Sujana Daniel Rinald Dian Ade Kurnia Dita Alviuni P Drajat, Hilmi Maulana Dyah Ayu Larasati Eka Nur'azmi Yunira Eko Dwi Nugroho Eko Dwi Nugroho Endo Pebri Dani Putra Fatihanursari Dikananda Fauzan Natsir Feri Fahrianto Filiana, Edinia Rosa Firmansyah, Hafiz Budi Gunawan, Rayhan Fatih Hanfiro, Pauline Hersa Dwi Yanuarso Ilham Firman Ashari Ilham Firman Ashari Jati Fatmawiyati Khusnul Khotimah Kurniawansyah, Apri Laisya, Nashwa Putri Leo Viranda Millennium Lisdayana, Nurmalisa M. Syamsuddin Wisnubroto M. Yafi Fahmi Madi Madi Marbun, Rustian Afencius Maria Oktarise Natania Gultom Marsista Buana Putri, Marsista Buana Mastuti Widianingsih, Mastuti Matdoan, Sakina Meida Cahyo Untoro Miranti Verdiana Muhamad Djuanda Muhammad Affandi Muhammad Iqbal Muhammad Nadhif Athalla Muhammad Yusuf Naufal Raki Nela Agustin Kurnianingsih Niken Paramita Nuk Ghurroh Setyoningrum Nur'azmi, Eka Oriza Zativalen, Oriza Perdana, Agung Mahadi Putra Praramadhana, Daffa Pratama, Dafa Septian Putra Pratama, Djourdi Amrida Pungki Resti Prabandari Putra, Gusty Nanda Kharisma Putri, Nabila Muthia Putri, Nabila Muthia Putty Yunesti Radhinka Bagaskara Rahman Indra Kesuma Rahmat Setiawan Raidah Hanifah Raidah Hanifah Ramadhan, Irzal Raisya Revangga, Dwi Arthur Rinaldi, Daniel Risfihan Rafi Salisu, Imam Auwal Samsu Bahri Sianturi, Elsa Elisa Yohana Silviana Silviana Sinaga, Nydia Renli Siregar, Abu Bakar Siddiq Sisilia Juli A Sophia Nouriska Sudiarjo, Aso Suranta, Akmal Fauzan Untoro, Meida Cahyo Untoro, Meida Cahyo Utoro, Meida Cahyo Vebera Maslami Verdiana, Miranti Wafiqah Azhar , Afra Winda Yulita Winda Yulita Yulita, Winda Yunira, Eka Nur'azmi Zainal Arifin