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All Journal Tekno : Jurnal Teknologi Elektro dan Kejuruan Techno.Com: Jurnal Teknologi Informasi Jurnal Sosioteknologi Jurnal Transformatika JUSIFO : Jurnal Sistem Informasi Jurnas Nasional Teknologi dan Sistem Informasi Sistemasi: Jurnal Sistem Informasi JMM (Jurnal Masyarakat Mandiri) SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Tecnoscienza JATI (Jurnal Mahasiswa Teknik Informatika) Journal of Advanced in Information and Industrial Technology (JAIIT) Sewagati Jurnal Informatika Polinema (JIP)
Endah Septa Sintiya
Politeknik Negeri Malang
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Sistem Pendukung Keputusan Pemilihan Latihan Gym Berbasis Fuzzy-AHP Terintegrasi LLM Andhani, Yasmine Navisha; sintiya, Endah Septa; Amalia, Eka Larasati
Jurnal Informatika Polinema Vol. 11 No. 4 (2025): Vol. 11 No. 4 (2025)
Publisher : UPT P2M State Polytechnic of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jip.v11i4.8036

Abstract

Kesadaran masyarakat terhadap pentingnya kebugaran fisik mendorong meningkatnya kebutuhan akan program latihan gym yang sesuai dengan kondisi kesehatan masing-masing individu. Namun, banyak klien yang masih kesulitan menentukan program latihan yang tepat, dan personal trainer sering menghadapi tantangan dalam memberikan rekomendasi personal secara efisien. Penelitian ini bertujuan untuk mengembangkan sistem pendukung keputusan pemilihan program latihan gym menggunakan metode Fuzzy AHP yang terintegrasi dengan Large Language Model (LLM). Sistem dikembangkan menggunakan metode Extreme Programming dan diimplementasikan dalam platform web berbasis Laravel. Metode Fuzzy AHP digunakan untuk memberikan bobot pada lima kriteria kesehatan utama FAT%, BMI, WHR, Blood Pressure, dan RHR terhadap empat alternatif program latihan, yaitu fat loss, kekuatan, daya tahan, dan kardio. LLM digunakan untuk memberikan rekomendasi tambahan yang bersifat deskriptif dan informatif berdasarkan hasil perankingan. Hasil pengujian menunjukkan bahwa sistem memperoleh nilai System Usability Scale (SUS) sebesar 75,00 yang termasuk dalam kategori “baik” dan tingkat akurasi sebesar 80% terhadap pertimbangan profesional, menunjukkan bahwa sistem memberikan rekomendasi yang relevan dan dapat diandalkan. Penelitian ini memberikan kontribusi nyata dalam mempermudah personal trainer dan klien dalam menentukan program latihan yang sesuai, serta berpotensi untuk diadaptasi dalam layanan kebugaran lainnya guna meningkatkan efisiensi dan personalisasi rekomendasi latihan.
Peningkatan efisiensi produksi melalui penerapan sistem informasi peramalan kebutuhan kacang kedelai di Pabrik Tahu Melati, Batu Sintiya, Endah Septa; Amanda, Sely Ruli; Ulfa, Farida; Subhi, Dian Hanifudin; Ikawati, Deasy Sandhya Elya; Pratama, Adevian Fairuz; Affandi, Luqman
SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Vol 9, No 5 (2025): September
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jpmb.v9i5.34110

Abstract

AbstrakPabrik Tahu Melati di Batu menghadapi permasalahan inefisiensi produksi akibat ketiadaan sistem yang memadai untuk meramalkan kebutuhan bahan baku kacang kedelai, yang berpotensi menimbulkan kerugian operasional. Kegiatan pengabdian ini bertujuan meningkatkan efisiensi produksi melalui perancangan dan penerapan sistem informasi peramalan berbasis website. Mitra sasaran adalah pemilik dan staf Pabrik Tahu Melati. Metode pelaksanaan mengunakan waterfall meliputi analisis kebutuhan, desain sistem, implementasi pengembangan aplikasi web dan model Double Exponential Smoothing di dalamnya, verifikasi, hingga pemeliharaan dengan pelatihan dan pendampingan. Hasil kegiatan menunjukkan peningkatan signifikan: secara kualitatif, mitra kini mampu mengoptimalkan manajemen persediaan dan membuat keputusan berbasis data. Secara kuantitatif, ketepatan pembelian bahan baku meningkat dari 60% menjadi 95%, frekuensi masalah stok menurun dari 5 kali menjadi 1 kali per bulan, dan staf operasional kini mampu mengoperasikan sistem dengan tingkat pemahaman yang naik dari rata-rata 1,75 menjadi 4,25. Hasil Kuesioner kepuasan mitra menujukkan skor 3,2 dari 4 atau 80% puas dengan pelaksanaan pengabdian ini. Kata kunci: efisiensi produksi; sistem informasi peramalan; double exponential smoothing; pengambilan keputusan berbasis data; UMKM. AbstractThe Melati Tofu Factory in Batu faces production inefficiency due to the absence of a system capable of anticipating soybean raw material requirements, which has the potential to cause operational losses. This community service activity aims to improve production efficiency through the design and implementation of a web-based forecasting information system. The target partners are the owners and staff of the Melati Tofu Factory. The implementation follows the waterfall method, covering requirement analysis, system design, web application development incorporating the Double Exponential Smoothing model, verification, and maintenance, along with training and mentoring. The results of the activity indicate significant improvements: qualitatively, the partners are now able to optimize inventory management and make data-driven decisions. Quantitatively, the accuracy of raw material purchases increased from 60% to 95%, the frequency of stock-related issues decreased from five times to once per month, and operational staff are now able to operate the system, with the average level of understanding increasing from 1.75 to 4.25. The partner satisfaction questionnaire results show an average score of 3.2 out of 4, indicating that 80% of the partners are satisfied with the outcomes of this community service. Keywords: production efficiency; forecasting information system; double exponential smoothing; data-driven decision-making; MSME.
Implementasi Machine Learning dalam Sistem Prediksi dan Rekomendasi Program Diet Terintegrasi LLM Sintiya, Endah Septa; Amanda, Sely Ruli; Bella Vista, Candra; Nugroho Pramudhita, Agung
Jurnal Nasional Teknologi dan Sistem Informasi Vol 11 No 2 (2025): Agustus 2025
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v11i2.2025.144-151

Abstract

Malnutrition, both in the form of overweight and underweight, remains a global health challenge. Unhealthy urban lifestyles and limited access to appropriate nutritional interventions exacerbate this problem. Technology-based approaches such as machine learning and Large Language Models (LLM) offer opportunities to improve the effectiveness of dietary management. This study proposes the development of a machine learning-based and LLM-integrated diet program prediction and recommendation system applied to Cafe NUT Castle. The system was developed to digitize body composition data recording, predict diet programs (weight loss, weight gain, and body fat loss) using the Random Forest algorithm, and generate personalized initial diet recommendations through the integration of the Gemini Flash-Lite API. Based on the test results, the prediction model achieved an accuracy of 93% on the test data and 84% on 50 new datasets. Evaluation of the diet recommendations generated by LLM showed a feasibility level of 86.6% which was categorized as very feasible. These results indicate that the developed system is not only accurate in predicting diet programs but also effective in providing initial recommendations that can support decision-making in digital nutrition consultation services.
Analyzing the Application of Optical Character Recognition: A Case Study in International Standard Book Number Detection Rozi, Imam Fahrur; Ananta, Ahmadi Yuli; Sintiya, Endah Septa; Amalia, Astrifidha Rahma; Ariyanto, Yuri; Nugraeni, Arin Kistia
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 2 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i2.4367

Abstract

In the era of advanced education, assessing lecturer performance is crucial to maintaining educational quality. One aspect of this assessment involves evaluating the textbooks authored by lecturers. This study addresses the problem of efficiently detecting International Standard Book Numbers (ISBNs) within these textbooks using optical character recognition (OCR) as a potential solution. The objective is to determine the effectiveness of OCR, specifically the Tesseract platform, in facilitating ISBN detection to support lecturer performance assessments. The research method involves automated data collection and ISBN detection using Tesseract OCR on various sections of textbooks, including covers, tables of contents, and identity pages, across different file formats (JPG and PDF) and orientations. The study evaluates OCR performance concerning image quality, rotation, and file type. Results of this study indicate that Tesseract performs effectively on high-quality, low-noise JPG images, achieving an F1 score of 0.97 for JPG and 0.99 for PDF files. However, its performance decreases with rotated images and certain PDF conditions, highlighting specific limitations of OCR in ISBN detection. These findings suggest that OCR can be a valuable tool in enhancing lecturer performance assessments through efficient ISBN detection in textbooks.
Systematic Literature Review: Implementation COBIT as a Best Practice of Electronic Based Government System Governance Puspitaningrum, Ari Cahaya; Fitrani, Laqma Dica; Sintiya, Endah Septa
Sistemasi: Jurnal Sistem Informasi Vol 13, No 1 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i1.3639

Abstract

Presidential Regulation of the Republic of Indonesia No. 95 of 2018 told that the implementation of SPBE is recommended to create clean, effective, transparent and accountable government governance as well as quality and trustworthy public services. This research use the systematic literature review (SLR) method by reviewed the implementation of COBIT best practices in various government organization in Indonesia in the last 5 years. This was done by analyzed each selected study and it can be concluded that there were 27 scientific journal studies and 3 conference studies. Analysis of 30 studies resulted in several groupings of studies, those are based on study objectives, frequently found focus areas, and frequently used domains. Based on the grouping of selected studies, it can be seen that there are 2 different objectives in using COBIT in government agencies, 1) the objective of measuring the level of maturity and capability of IT governance and 2) the objective of designing an IT governance system. The domains most frequently found in the selected studies are APO and DSS. Apart from that, this research also found a focus area that is often found, namely IT services and there are recommendations for further research based on being able to evaluate and design IT governance in the COBIT domain which is often found based on the focus area.
Analisis Efektivitas Algoritma K-Means Clustering dalam Pengelompokan Siswa Berdasarkan Kemampuan Multidimensi Rafandi, Hanif Naufal; Nurhasan, Usman; Sintiya, Endah Septa
Techno.Com Vol. 24 No. 4 (2025): November 2025
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v24i4.14653

Abstract

Pengelompokan siswa berbasis data sangat penting untuk mendukung evaluasi yang adil dan menyeluruh, mengingat penilaian potensi selama ini cenderung terfokus pada aspek akademik saja. Penelitian ini mengembangkan sistem rekomendasi regu inti lomba kepramukaan menggunakan algoritma K-Means Clustering, dengan dataset berisi 120 siswa SMP yang dinilai berdasarkan parameter akademik, non-akademik, serta pencapaian SKU dan SKK. Jumlah cluster ditentukan sebanyak 24, sesuai dengan kategori lomba berdasarkan aturan Kwarnas mengenai lomba pramuka tingkat penggalang. Proses pengolahan data meliputi normalisasi dan reduksi dimensi menggunakan Principal Component Analysis (PCA). Evaluasi kualitas clustering dilakukan menggunakan metrik Silhouette Score dan Davies–Bouldin Index (DBI). Hasil terbaik diperoleh pada konfigurasi random_state = 42, n_init = 20, dan max_iter = 300, dengan Silhouette Score sebesar 0,1238 dan DBI sebesar 1,4418. Meskipun kualitas pengelompokan tergolong rendah dengan hasil Silhoutte Score = 0.102 dan DBI = 1.362, sistem ini tetap memberikan solusi objektif bagi pembina dalam memilih siswa berpotensi secara adil dan menyeluruh. Sistem ini juga menjawab keluhan orang tua terkait ketidakterlibatan anak dalam lomba, karena pemilihan dilakukan berdasarkan potensi keseluruhan kategori lomba, bukan hanya satu kategori untuk membentuk tim regu inti pramuka.   Kata kunci: K-Means Clustering, Principal Component Analysis (PCA), Silhouette Score, Davies–Bouldin Index, Regu Inti Pramuka.
Co-Authors Adevian Fairuz Pratama Affandi, Luqman Ahmadi Yuli Ananta Amalia, Astrifidha Rahma Amanda, Sely Ruli Ananta, Ahmadi Yuli Andhani, Yasmine Navisha Anugrah Nur Rahmanto Apol Pribadi Apol Pribadi Ari Cahaya Puspita Ningrum Ari Cahaya Puspitaningrum Ari Cahaya Puspitaningrum Ari Cahaya Puspitaningrum Ari Cahaya Puspitaningrum Ari Cahaya Puspitaningrum Arin Kistia Nugraeni Ashri Shabrina Afrah Astrifidha Rahma Amalia Bekti Cahyo Hidayanto Cahaya Puspitaningrum, Ari Candra Bella Vista Dhebys Suryani Dian Hanifudin Subhi Dimas Wahyu Wibowo Dodit Suprianto Eka Larasati Amalia Ekojono Ekojono Ekojono, Ekojono Erfan Rohadi Farida Ulfa Fayra, Syahla' Syafiqah Feby Artwodini Muqtadiroh Fitrani, Laqma Dica Furqon, Muhammad Ariful Hidayah, Hilda Khoirotul Ikawati, Deasy Sandhya Elya Imam Fahrur Rozi Imam Fahrur Rozi Irmasari Hafidz Irsyad Arif Mashudi Izzati, Afifah Nurul Khakim Ghozali Kusumawardana, Arya Latif, Ummi Khaira Luqman Affandi Luqman Affandi Mahfud Jiono Mohammad Al Hafidz Muhammad Afnan Habibi Najwa, Nina Fadilah Nugraeni, Arin Kistia Nugroho Pramudhita, Agung Pramudhita, Agung Pratama, Adevian Fairuz Puspitaningrum, Ari Cahaya Rafandi, Hanif Naufal Riskitasari, Septyana Rita Sari Rita Sari Rizdania, Rizdania Sely Ruli Amanda Sofyan Noor Arief Sulthon Fuad P., Aldhihamda Suryani, Dhebys Tony Dwi Susanto Ulfa, Farida Unggul Pamenang, Muhammad Usman Nurhasan Yuri Ariyanto Yuri Ariyanto