Article Per Year (5 Year)
p-Index From 2021 - 2026
3.006
P-Index
This Author published in this journals
All Journal PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta JIKO (Jurnal Informatika dan Komputer) Informatics for Educators and Professional : Journal of Informatics Jurnal Teknik Informatika STMIK Antar Bangsa Jurnal Sisfokom (Sistem Informasi dan Komputer) Petir IJISTECH (International Journal Of Information System & Technology) JISICOM (Journal of Information System, Infomatics and Computing) Jurnal Mantik Jurnal Ilmu Komputer dan Bisnis Jurnal Sistem Komputer dan Informatika (JSON) Infotek : Jurnal Informatika dan Teknologi Jurnal Teknik Informatika (JUTIF) International Journal of Educational Research and Social Sciences (IJERSC) SPEED - Sentra Penelitian Engineering dan Edukasi Journal of Innovation Research and Knowledge Paradigma Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Jurnal Riset Sistem Informasi
Suhardjono S
Universitas Bina Sarana Informatika, Indonesia
Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Social Sciences Other

Published : 24 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 24 Documents
Search

 1   2   3 
Optimalisasi Prediksi Dalam Kelulusan Berbasis Deep Learning: Perbandingan Kinerja Multi-Layer Perceptron dan Deep Neural Network Dewi, Yumi Novita; Iqbal, Muhammad; Lisnawanty; Maisyaroh; Suhardjono
Infotek: Jurnal Informatika dan Teknologi Vol. 8 No. 2 (2025): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v8i2.30756

Abstract

Predicting on-time graduation is one of the significant challenges in education, aiming to model the factors influencing academic success. This study aims to compare the performance of two Deep Learning algorithms, namely Deep Neural Networks (DNN) and Multi-Layer Perceptron (MLP), in predicting on-time graduation. The methodology used involves evaluating both algorithms with various performance metrics, including Recall, Accuracy, Precision, AUC, MCC, and Cohen Kappa. The results show that DNN performs better in terms of Recall (0.9766), indicating its ability to capture most of the students who graduate on time, although its AUC (0.8625) and Precision (0.8803) are lower compared to MLP. On the other hand, MLP excels in Accuracy (0.8812) and Precision (0.9037), providing more stable results for MCC and Cohen Kappa, demonstrating a better balance in predicting students who graduate on time and those who do not. Overall, while DNN is more sensitive in capturing students who graduate on time, MLP performs better in terms of balance between accuracy and minimizing prediction errors. This study suggests using MLP if the primary priority is accuracy and prediction stability, while DNN is more suitable when the main focus is capturing as many students as possible who graduate on time.
Model Prediktif Keterlambatan Pembayaran Mahasiswa Berbasis Seleksi Fitur dengan Particle Swarm Optimization Desvia, Yessica Fara; Suharjanti; Suhardjono; Irmawati Carolina; Resti Lia Andharsaputri
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 2 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i2.8973

Abstract

Keterlambatan pembayaran biaya kuliah menjadi salah satu permasalahan krusial di perguruan tinggi swasta yang dapat berdampak pada risiko akademik, seperti cuti atau putus studi. Penelitian ini diarahkan untuk mengembangkan model prediktif dalam mengidentifikasi keterlambatan pembayaran oleh mahasiswa, dengan memanfaatkan algoritma klasifikasi Decision Tree dan Random Tree, serta menerapkan metode Particle Swarm Optimization (PSO) untuk proses seleksi fitur. Data yang digunakan dalam penelitian ini mencakup 15.697 mahasiswa, masing-masing memiliki enam atribut sebagai variabel prediktor serta satu atribut target yang menunjukkan status mahasiswa, yaitu aktif atau cuti. Tahapan penelitian mencakup pengumpulan data, pra-pemrosesan, klasifikasi, seleksi fitur, dan evaluasi model dilakukan dengan menggunakan metrik akurasi, serta kurva ROC dan nilai AUC. Hasil penelitian menunjukkan akurasi model mencapai 98,83%, dengan peningkatan signifikan AUC pada Random Tree dari 0,632 menjadi 0,825 setelah seleksi fitur menggunakan PSO. Temuan ini menunjukkan bahwa PSO efektif dalam meningkatkan performa model klasifikasi dan mengurangi kompleksitas fitur yang tidak relevan. Sistem prediktif yang dihasilkan dapat membantu institusi pendidikan dalam melakukan deteksi dini mahasiswa berisiko menunggak, sehingga memungkinkan pengambilan tindakan preventif dan intervensi lebih tepat sasaran untuk mendukung keberlangsungan akademik mahasiswa.
New Technology in Automated Vehicles to Improve Passenger Safety Suhardjono, Suhardjono; Priyono, Priyono; Sri Iswiyanti, Agus; Parulian, Dudi; Syah Putra, Arman; Aisyah, Nurul
International Journal of Educational Research & Social Sciences Vol. 2 No. 3 (2021): June 2021
Publisher : CV. Inara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51601/ijersc.v2i3.96

Abstract

The background of this research is by prioritizing how to improve the safety of passengers on a vehicle with increased security so that if an accident occurs, the passenger does not suffer any injury. If necessary, it is not scratched on the body. With this research, it is necessary to increase security in order to provide maximum protection. for passengers and motorists. The method used in this study using the literature review method based on research that has been done previously so that it can be the basis for this research. With the literature review, the research will be able to find new research problems so that this research can be the latest research in order to serve as the basis for future research. In this study, we will find out how to protect passengers on a vehicle with ways that passengers can do so that the security side can be improved. Therefore, the use of security in a vehicle is very important so that it can help drivers and passengers in driving. In this study will produce a proposed system that can be used as a basis as a guide in order to protect passengers and motorists and can improve the safety side of driving.
Application Design "Test Job Application" On Android OS Using The AHP Algorithm Suharjono; Hari Sugiarto; Istiqomah Sumadikarta; Muhammad Ryansyah; Muhammad Hilman Fakhriza; Arman Syah Putra
International Journal of Educational Research & Social Sciences Vol. 2 No. 5 (2021): October 2021
Publisher : CV. Inara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51601/ijersc.v2i5.185

Abstract

The background of this research is how to make an application that makes it easier for job seekers to find work with an Android-based application method so that it can be done anywhere and anytime with a very small quota. Helped and employers and companies will also be helped. The method used in this research is to use the method of studying literature or literature by reading many journals related to this research, after that make a prototype so that it can be given an appearance. This research will be able to see whether it is successfully used or not. The problem raised in this research is how to help job seekers find work without leaving the house and being able to search for jobs around the world using only an Android based application that can be done from home. This research produces a prototype system that will be made in the future, so that it can help workers in finding work and companies in finding workers.
Co-Authors Abdul Hamid Abdul Hamid Abdul Hamid ADE RISNA SARI Adjat Sudradjat Ainayah Al Fathiyyah Andri Yansah Anus Wuryanto Arman Syah Putra Arman Syah Putra Arman Syah Putra Cakranegara, Pandu Adi Desvia, Yessica Fara Dewi Yuliandari Firstianty Wahyuhening Fibriany Ganda Wijaya Ganda Wijaya, Ganda Gilang Purnama Adji Hafis Nurdin Hari Sugiarto - UBSI Hendra Hendra Herlina, Maria Hermanto Hermanto Hermanto Hilmiah Indah Ariyati Irmawati Carolina ispandi ispandi Istiqomah Sumadikarta Jajang Jaya Purnama Kholifah, Desiana Nur Lisnawanty Maisyaroh Mochammad Abdul Azis Muhammad Fahmi Muhammad Faisal Muhammad Hilman Fakhriza Muhammad Iqbal Muhammad Ryansyah Nanny Mayasari Nurul Aisyah Nurul Aisyah Nurul Aisyah Olivia, Shinta Parulian, Dudi Priyono Priyono Priyono Priyono Resti Lia Andharsaputri Resti Pebrina Ridwansyah Ridwansyah Ridwansyah Rino Ramadan Rio Wirawan Rohimah, Luthfia Rudy Max Damara Gugat Sri Iswiyanti, Agus Suhardjono Suhardjono suharjanti Suryadhitia, Rachmat Syah Putra, Arman V.H. Valentino Wahana Indra Komala Wahid, Bilal Abdul Yumi Novita Dewi