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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer JAIS (Journal of Applied Intelligent System) Faktor Exacta Jurnal Informatika Universitas Pamulang Jurnal Tekno Insentif Jurnal Informatika SIMANTIK JSI (Jurnal sistem Informasi) Universitas Suryadarma Journal of Information System, Applied, Management, Accounting and Research Progresif: Jurnal Ilmiah Komputer Jurnal Teknologi Informatika dan Komputer Journal of Computer Networks, Architecture and High Performance Computing SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) Bulletin of Computer Science Research PROFICIO: Jurnal Pengabdian Masyarakat Bulletin of Information Technology (BIT) International Journal Software Engineering and Computer Science (IJSECS) Academic Journal of Computer Science Research (AJCSR) Pelita Teknologi : Jurnal Ilmiah Informatika, Arsitektur dan Lingkungan Jurnal Ilmiah SIGMA: Informatics Engineering Journal of UPB Jurnal Informatika Teknologi dan Sains (Jinteks) Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Pengabdian West Science SABAJAYA Jurnal Pengabdian Kepada Masyarakat VIDHEAS: Jurnal Nasional Abdimas Multidisiplin Jurnal Pelita Pengabdian SAINTEK Jurnal Indonesia : Manajemen Informatika dan Komunikasi Jurnal Pengabdian Nasional (JPN) Indonesia Journal of Informatics and Information Security JIS
Nawangsih, Ismasari
Universitas Pelita Bangsa
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Public Health Social Sciences Other

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Penerapan dan Pelatihan Sistem Pembayaran Sekolah di SMK Brahari sebagai Upaya Transformasi Layanan Keuangan Sekolah Nawangsih, Ismasari; Wiyatno, Tri Ngudi; Widodo , Edy; Budiarto , Eko; Majid, Annisa Maulana
Jurnal Pengabdian Nasional (JPN) Indonesia Vol. 7 No. 1 (2026): Januari
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63447/jpni.v7i1.1669

Abstract

Digital transformation in the education sector demands increased efficiency and transparency in school financial management. However, many schools, including SMK Brahari, still rely on manual payment systems that are prone to errors and lack transparency. The identified gap is the absence of an integrated and user-friendly digital payment system accessible to all stakeholders. This community service activity aimed to implement and provide training on a digital school payment system for 20 administrative and educational staff. The methods included initial observation, development of a simple system, hands-on training, and implementation assistance. Evaluation results showed that 85% of participants understood the system workflow, 90% found it helpful in simplifying their tasks, and all participants expressed readiness to implement it. The system proved effective in improving efficiency, transparency, and ease of payment monitoring, marking an important step toward transforming school financial services.
Implementasi Sistem Informasi Manajemen (SIM) Alat Pemadam Kebakaran (APAR) Berbasis Web dan QR Code di PT. Pandu Sejahtera Utama Budi Rahardjo, Sugeng; Rahayu, Siti; W, Wiyanto; Nawangsih, Ismasari; Al Bina, Fiqhy Faradisa
VIDHEAS: Jurnal Nasional Abdimas Multidisiplin Vol. 3 No. 2 (2025): Desember 2025
Publisher : VINICHO MEDIA PUBLISINDO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61946/vidheas.v3i2.161

Abstract

Penggunaan Alat Pemadam Api Ringan (APAR) di tempat kerja merupakan sebuah kewajiban seperti tertuang pada Peraturan Menteri Tenaga Kerja No. 04 Tahun 1980 yang mewajibkan penyediaan dan pemeliharaan untuk memastikan keselamatan sesuai potensi bahaya kebakaran. Menurut Dinas Pemadam Kebakaran (Damkar) Kabupaten Bekasi yang di kutip dari laman Pemkab Bekasi [1] mencatat peningkatan signifikan dalam penanganan kasus penyelamatan dan evakuasi masyarakat sepanjang tahun 2025. Hingga awal September, sudah terdapat 235 kasus penyelamatan yang ditangani, jauh lebih tinggi dibanding 71 kasus kebakaran yang tercatat sejak Januari hingga Juli. PT Pandu Sejahtera Utama (PSU), berlokasi di Kampung Walahir RT 001/RW 002, Desa Karangbahagia, Kecamatan Cikarang Utara, turut berkontribusi dalam bidang perdagangan umum, kontraktor, industri pengolahan, konstruksi, dan jasa, dengan fokus utama pada pengadaan APAR jenis dry chemical. Dengan omset bulanan Rp150 juta, perusahaan mencatat penjualan sekitar 50 tabung APAR baru dan 500 layanan refill per bulan dari 240 pelanggan aktif, setiap bulan, dua pelanggan keluar akibat lambatnya respons layanan, sementara hanya lima pelanggan baru bergabung, menunjukkan rendahnya retensi dan akuisisi pelanggan terletak pada sistem manajemen dokumentasi yang lemah. Pengelolaan data pelanggan, data refill, data APAR, dan data pengecekan tidak terorganisasi dengan baik, menyebabkan ketidakakuratan informasi, keterlambatan layanan, dan hilangnya kepercayaan pelanggan, terkait masalah tersebut, bekerja sama dengan pihak kampus Universitas Pelita Bangsa, dibuatlah Sistem Informasi Manajemen APAR Berbasis Web dan QR Code pada PT Pandu Sejahtera Utama, dimana Sistem ini (1) Manajemen Inventarisasi dan Pemeliharaan APAR, Aplikasi memungkinkan pencatatan data APAR (lokasi, jenis, jumlah, tanggal kadaluarsa, dan riwayat perawatan) secara terpusat (2) Otomatisasi Jadwal Inspeksi dan Pengingat dengan bantuan Aplikasi (3) Optimalisasi Layanan Pelanggan. Software memungkinkan pengelolaan database klien, riwayat layanan, dan komunikasi (4) Peningkatan Efisiensi Operasional dan Biaya. Sistem ini di bangun sejak Oktober 2025 dan selesai Desember 2025, Uji coba di Perusahaan selama 2 minggu selama bulan Januari 2026 dan berfungsi efektf.
Implementasi Algoritma K-Means Pada Sistem Persediaan Barang Khaliq, Achsyanul; Nawangsih, Ismasari; Majid, Annisa Maulana
Bulletin of Computer Science Research Vol. 6 No. 2 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i2.1017

Abstract

Inventory is an important component in a company's operational activities, especially in the trade sector, because it directly affects the smoothness of sales and the level of customer satisfaction. Unstructured inventory management can lead to stockpiling of goods, stock shortages, and inappropriate decision making. Maisa Building Materials Store located in Semerap, Kerinci Regency, Jambi, currently still records inventory manually, so the shop owner has difficulty in identifying items with high and low sales levels. This study aims to implement the K-Means Clustering algorithm in grouping inventory based on sales levels to support more effective and efficient stock management. The research method used is data mining with the stages of data collection, preprocessing, manual calculation of the K-Means algorithm, and implementation using RapidMiner software. The analyzed data amounted to 507 inventory items that have gone through a data cleaning process so that they are suitable for use in grouping. Grouping is done with two clusters, namely a cluster of goods with a low sales level and a cluster of goods with a high sales level. The results of the study indicate that 494 items, or 97.44 percent, fall into the low-sales cluster, while 13 items, or 2.56 percent, fall into the high-sales cluster. These results indicate that most products have relatively low sales turnover, while only a small proportion contribute significantly to total store sales. The information generated from this clustering process can be used as a basis for decision-making in inventory management, particularly in determining stocking priorities, stock control, and developing appropriate, data-driven marketing strategies.
Penerapan Explainable AI dan Model Stacking Untuk Mengindentifikasi Faktor Risiko Stunting Balita Majid, Annisa Maulana; Nawangsih, Ismasari
Progresif: Jurnal Ilmiah Komputer Vol 22, No 1 (2026): Januari
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v22i1.3549

Abstract

Stunting remains a nutritional problem in children that is difficult to detect early due to its multifactorial causes and the limitations of conventional analytical approaches. This study aims to develop a stacking-based Machine Learning model for stunting prediction and to implement Explainable Artificial Intelligence (XAI) to interpret the risk factors influencing the prediction outcomes. The methods employed include Decision Tree, Random Forest, and Support Vector Machine (SVM) as base learners, and Logistic Regression as the meta-learner in an ensemble stacking framework, with performance evaluated using accuracy, precision, recall, and F1-score. The results indicate that the Decision Tree algorithm and the Stacking method achieved the best performance with 100% accuracy, while the XAI analysis identified current body weight, birth weight, and age as the primary factors influencing stunting prediction.Keywords: Stunting; Stacking; Machine Learning; Explainable Artificial Intelligence AbstrakStunting masih menjadi permasalahan gizi pada anak yang sulit dideteksi secara dini karena dipengaruhi oleh berbagai faktor dan keterbatasan analisis konvensional. Penelitian ini bertujuan mengembangkan model stacking berbasis Machine Learning untuk prediksi stunting serta menerapkan Explainable Artificial Intelligence (XAI) dalam menginterpretasikan faktor-faktor risiko yang berpengaruh terhadap hasil prediksi. Metode yang digunakan meliputi algoritma Decision Tree, Random Forest, Support Vector Machine (SVM) sebagai base learner dan Logistic Regression sebagai meta learner pada ensemble stacking, dengan evaluasi menggunakan accuracy, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa algoritma Decision Tree dan penerapan metode Stacking menghasilkan kinerja terbaik dengan tingkat akurasi 100%, sementara analisis XAI mengidentifikasi bahwa berat badan, BB lahir, dan usia merupakan faktor utama yang memengaruhi prediksi stunting.Kata kunci: Stunting; Stacking; Machine Learning; Explainable Artificial Intelligence
Co-Authors Adi Rusdi Widya Adi Tio Ilhasa Agus Setiawan Agus Suwarno, Agus Ahmad Gunawan Ahmad Tholud Ahmad Tholud2 Ahmad, Asyari Al Bina, Fiqhy Faradisa Amali Amali Amali, Amali Andri Firmansyah Anggi Muhammad Rifa’i Annisa Maulana Majid Annisa Maulana Majid Antika Zahrotul Kamalia Antika Zahrotul Kamalia Ardi Gunawan Arfan, Ibnu Soffi Asep Arwan Sulaeman Asti Setyaningsih Asty Setyaningsih Avifah Dian Permatasari Badruzzaman, Aceng Budi Rahardjo, Sugeng Budiarto , Eko Budiarto, Eko Cecep Wiranto chandra H, Desy Djoko Nugroho Donny Maulana Edora Edora Edy Widodo Edy Widodo Elgi Ginanjar Fazri Setyawan Ferawati, Eva Gatot Tri Pranoto Hari Puji Saputro Indradewa, Rhian Ismamudi Ismamudi Ismamudi, Ismamudi Junisa Sahar Karina Imelda Khaliq, Achsyanul Kurniadi, Nanang Tedi Majid, Annisa Maulana Majid, Annisa Maulana Makarim, Ziddan Maulana Majid, Annisa Maulana, Donny Miftahul Jannah Miftakul Huda Muhamad Adhi Mukti Naya, Candra Pupung Purnamasari Pupung Purnamasari Putri Maharani, Nanda Rachmat Hidayat Rahardjo, Sugeng Budi Reza Puspita Riady, Sasmitoh Rahmad Rianti Kinasih Rismawati Sanudin Sanudin Sellina, Sesri Septian Arie Prayoga Sifa Fauziah Siti Rahayu SITI SETIAWATI Soer, U. Darmanto Soer, U. Darmanto Sri Rejeki Sugeng Budi Rahardjo Sugeng Budi Raharjo Suherman Suherman Sulaeman, Asep Arwan Supriyati . Suriyanti Susanto, Dede Agus Suwaryo, Niko Tedi, Nanang Tri Ngudi Wiyatno W, Wiyanto Wahyu Hadikristanto Widodo , Edy Widya, Adi Rusdi Wiyanto Wiyanto Wiyanto Wiyatno, Tringudi Zed, Etty Zuliawati Zuliawati zed, Etty