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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Conference SENATIK STT Adisutjipto Yogyakarta Infotech: Jurnal Informatika & Teknologi Advance Sustainable Science, Engineering and Technology (ASSET)
Eko Abdul Goffar
Politeknik Astra
Aerospace Engineering Chemistry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Analysis and Design Information System of Recapitulation Jam Minus P5M Eko Abdul Goffar; Radix Rascalia; Rida Indah Fariani
SENATIK STT Adisutjipto Vol 5 (2019): Peran Teknologi untuk Revitalisasi Bandara dan Transportasi Udara [ISBN 978-602-52742-
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/senatik.v5i0.348

Abstract

Pertemuan 5 Menit (P5M) is a routine activity that is always carried out by instructor in Politeknik Manufaktur Astra (Polman Astra) to students in each Unit Pelaksana Teknis (UPT) before or after lectures begin. Students who do not follow or violate the rules at the time of P5M will get a penalty in the form of minus hours. Minus hours are penalties given to students. Data minus hours of P5M are recorded using a form that is recapitulated one by one for every week and inputted into academic information system  Polman Astra. The input time for minus P5M clock data is 565 minutes. Therefore we need an information system that can help overcome or automate it. In this research an analysis and design will be carried out using the System Development Life Cycle (SDLC) stages with extreme programming methodology which will reduce the recapitulation time from 565 to 280 minutes.
Development An Android Based Pemeriksaan 5 Menit (P5M) Information System Kristanto, Candra Bagus; Goffar, Eko Abdul; Rascalia, Radix
SENATIK STT Adisutjipto Vol 5 (2019): Peran Teknologi untuk Revitalisasi Bandara dan Transportasi Udara [ISBN 978-602-52742-
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/senatik.v5i0.343

Abstract

P5M is a readiness inspection activity such as the attributes and attendance of students before practicing. Students whose attributes are incomplete will be recorded in P5M sheet. Each attribute has a violation point or hour minus. P5M data, will recapitulate the number of hours minus. Constraints that occur in the Astra Manufacturing Polytechnic, namely the filling process of P5M sheets is still manual. This causes the time required to recapitulate minus hours to be long, which is on average 5 minutes. This causes the process of renewing the number of hours minus students and the process of paying hours minus being hampered. To overcome this, an Android-based P5M information system was made. This information system was developed using the Extreme Programming methodology and the MySQL database. With the P5M information system, it can eliminate the process of recapitulation of minus hours and reduce paper usage.
Stunting Prediction Modeling in Toddlers Using a Machine Learning Approach and Model Implementation for Mobile Application Abdul Goffar, Eko; Eliviani, Rosa; Ayu Wulandhari, Lili
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 3 (2025): June 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i3.6450

Abstract

Children’s health and development are critical for maintaining national productivity and independence, with stunting being a major concern. Stunting, a form of malnutrition, impairs growth and development, affecting millions of people globally, including a significant number in Indonesia. This study addresses the challenge of stunting by developing a predictive model using machine learning techniques to forecast stunting risks based on public health data. The literature review section discusses the factors that influence stunting, and these factors are used as features to build a stunting prediction model. Then the features were used to build a model with three machine learning algorithms Extreme Gradient Boosting (XGBoost), Random Forest, and K-Nearest Neighbor (KNN) to build and evaluate models that predict stunting. The models were trained and assessed using public datasets and the most effective algorithm was integrated into a mobile application for practical use. The results indicate that the XGBoost model outperforms the other models with an accuracy of 85%, making it the optimal choice for implementation in a mobile application. The next-best model is selected to be implemented through a mobile application so that users can directly use the model that has been built. This application aims to enhance early detection and intervention efforts for stunting, potentially improving child health outcomes and contributing to long-term productivity by building predictive models and implementing the models into a mobile application. This study contributes to the implementation of models built using public data for application in mobile applications.
Assensing the Impact of Advanced Driver Assistance Systems (ADAS) on Road Safety an Empirical Study Using Factor Analysis Pramono, Anang; Eko Abdul Goffar
Advance Sustainable Science Engineering and Technology Vol. 7 No. 4 (2025): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v7i4.2371

Abstract

Road traffic accidents remain a critical global concern, especially in low- and middle-income countries. Advanced Driver Assistance Systems (ADAS) are introduced as proactive safety technologies in the industry 4.0 era. This study aims to assess the impact of ADAS on road safety through an empirical approach. A quantitative survey involving 260 licensed drivers was conducted, followed by qualitative interviews to provide contextual insights. The dataset was confirmed reliable (KMO = 0.82; Cronbach’s α = 0.87), and factor analysis identified four latent constructs: Collision Avoidance, Driver Behavior and Acceptance, System Reliability, and Road Safety Impact, explaining 68.5% of variance. Results indicate that collision avoidance and system reliability are the strongest predictors of user trust, while road safety impact emerges as an independent factor emphasizing societal benefits. The findings highlight that ADAS adoption should be framed not only as technological acceptance but also as a contribution to sustainable mobility and SDG 3.
Pemodelan sistem rekomendasi judul tugas akhir mahasiswa berdasarkan personalisasi mahasiswa tingkat akhir dan implementasi model berbasis situs web Eko Abdul Goffar; Dwi Diana Wazaumi
INFOTECH : Jurnal Informatika & Teknologi Vol 7 No 1 (2026): INFOTECH: Jurnal Informatika & Teknologi
Publisher : LPPMPK - Universitas Muhammadiyah Cileungsi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37373/infotech.v7i1.2139

Abstract

Mahasiswa tingkat akhir sering mengalami kesulitan dalam menentukan judul dan tujuan Tugas Akhir yang sesuai dengan minat, kompetensi, dan bidang keilmuan. Permasalahan ini didukung oleh hasil survei terhadap 30 mahasiswa program studi teknologi informasi melalui kuesioner daring, yang menunjukkan bahwa sebagian besar responden mengalami kendala dalam pemilihan topik Tugas Akhir akibat keterbatasan referensi yang relevan dan terarah. Meskipun literatur ilmiah tersedia dalam jumlah besar, belum terdapat sistem yang secara khusus membantu mahasiswa dalam merekomendasikan judul Tugas Akhir secara personal dan berbasis data. Penelitian ini mengembangkan model sistem rekomendasi judul Tugas Akhir menggunakan pendekatan content-based filtering berbasis pembelajaran mesin. Tahapan pengembangan meliputi pra-pemrosesan data berupa ekstraksi fitur teks dan pemodelan profil mahasiswa, yang selanjutnya dihitung tingkat kesamaannya menggunakan metode cosine similarity. Evaluasi model dilakukan menggunakan metrik Precision at K dengan nilai K = 10 dan menghasilkan tingkat presisi sebesar 99% pada data uji. Kontribusi penelitian ini terletak pada perancangan model rekomendasi yang mengintegrasikan profil mahasiswa dan konten referensi ilmiah secara efektif. Model yang dikembangkan diimplementasikan dalam prototipe berbasis web menggunakan framework Flask sebagai alat bantu penentuan judul Tugas Akhir.
Co-Authors Ayu Wulandhari, Lili Dwi Diana Wazaumi Eliviani, Rosa Kristanto, Candra Bagus Pramono, Anang Radix Rascalia Radix Rascalia, Radix Rida Indah Fariani