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All Journal Jurnal Ilmu Komputer Ilmu Administrasi Publik Jurnal Informatika dan Teknik Elektro Terapan Indonesian Journal on Software Engineering (IJSE) Jurnal Teknik Komputer AMIK BSI JURNAL MANAJEMEN INFORMATIKA (JUMIKA) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jiko (Jurnal Informatika dan komputer) JISICOM (Journal of Information System, Infomatics and Computing) Jurnal Mantik Technologia: Jurnal Ilmiah Jurnal Informatika dan Rekayasa Elektronik CERMIN: Jurnal Penelitian Buletin Poltanesa Jurnal Responsif : Riset Sains dan Informatika Computer Science (CO-SCIENCE) SPEED - Sentra Penelitian Engineering dan Edukasi Jurnal Interkom : Jurnal Publikasi Ilmiah Bidang Teknologi Informasi dan Komunikasi Jurnal Informatika Teknologi dan Sains (Jinteks) Journal Indonesia Law and Policy Review (JILPR) Jurnal Administrasi Publik Jurnal Multidisiplin Sahombu SATIN - Sains dan Teknologi Informasi Journal of Informatics, Electrical and Electronics Engineering Prawara Jurnal Pengabdian Masyarakat
Andi Taufik
Universitas Bina Sarana Informatika
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering 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 Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Public Health Social Sciences Other

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Journal : Journal of Informatics, Electrical and Electronics Engineering

 1 
Pengembangan Aplikasi Absensi Mobile Terintegrasi dengan Sistem Backoffice Berbasis Web Menggunakan Metode Pengembangan Perangkat Lunak Waterfall Nasya Putri Restyarna; Andi Taufik; Eko Setia Budi
Journal of Informatics, Electrical and Electronics Engineering Vol. 5 No. 1 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jieee.v5i1.2504

Abstract

Attendance systems play a crucial role in human resource management by serving as the foundation for accurate and efficient employee presence tracking. PT Urun Bangun Negeri previously used a manual attendance method that was prone to data errors, manipulation, and inefficiencies in reporting. This study aims to develop a digital attendance application based on Android that integrates with a web-based backoffice system. The software development method used is the Waterfall model, consisting of requirement analysis, system design, implementation, and testing phases. Kotlin was used to develop the mobile application, while Laravel was used for the web system. Key features include GPS-based location validation and selfie verification for attendance authenticity. System testing using Blackbox and User Acceptance Test (UAT) methods showed that the application performed well, with a success rate of more than 95% and a very high level of user satisfaction. These results demonstrate that the developed system effectively enhances efficiency, accuracy, and transparency in the company’s attendance process.
Klasifikasi Penerimaan Peserta Didik Baru Berdasarkan Sistem Zonasi Menggunakan Algoritma K-Nearest Neighbors Syah Fiqri, Muhammad; Andi Taufik
Journal of Informatics, Electrical and Electronics Engineering Vol. 5 No. 1 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jieee.v5i1.2595

Abstract

The implementation of the zoning system in student admission (PPDB) often raises challenges in determining eligibility based on domicile, requiring a data-driven approach to support the selection process. This study aims to classify new students of SMPN 16 Bogor for the 2025 academic year using the K-Nearest Neighbors (KNN) algorithm. The dataset consists of 1,153 student records with attributes including longitude, latitude, distance from home to school, and zoning labels. Preprocessing involved data cleaning, label encoding, and feature standardization before splitting the data into 75% training and 25% testing sets. The optimal parameter was found at K=18 with a minimum error rate of 0.1591. Experimental results showed an accuracy of 97% for training data and 84% for testing data, indicating that the model performs reasonably well despite signs of overfitting. This research contributes by demonstrating that spatial attributes can be effectively integrated into zoning-based classification and provides a foundation for developing more objective and adaptive decision support systems in the context of student admissions.
Pemodelan Prediksi Volume Penumpang Transjakarta Menggunakan Regresi Pada Algoritma Machine Learning Wijaya, Ilham Maulana; Taufik, Andi
Journal of Informatics, Electrical and Electronics Engineering Vol. 5 No. 1 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jieee.v5i1.2648

Abstract

The rapid population growth and urbanization in Jakarta pose significant challenges to the provision of efficient public transportation, particularly for Transjakarta, which often experiences fluctuating passenger volumes that complicate capacity management and operational efficiency. This study aims to model and predict Transjakarta passenger volumes using regression methods within machine learning algorithms, by comparing three models: Linear Regression, Random Forest Regression, and Gradient Boosted Trees Regression. The dataset consists of historical passenger records from routes S21 (Ciputat–CSW/Tosari) and S22 (Ciputat–Kampung Rambutan) covering the period from January 2022 to March 2025. The data were processed through several stages, including preprocessing, categorical variable transformation, train-test splitting, and model evaluation using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²). The results show that Gradient Boosted Trees Regression achieved the best predictive performance with an R² of 0.73 and an average error of approximately 22,000 passengers, outperforming Linear Regression (R² = 0.65) and Random Forest Regression (R² = 0.63). These findings highlight that ensemble boosting is more effective in capturing non-linear patterns in passenger data, making it the most suitable predictive model to support operational planning, fleet efficiency, and the development of adaptive and sustainable public transportation policies.
Co-Authors A. St. Nuraeni Abdussomad, Abdussomad Ade Christian Ahmad, Hanief Fathul bahri Andreyestha Andreyestha Anugerah Christian Arief, Muhammad Makka Arnika, Mita Atifah Arsanti Bambang Wijonarko Daniel Kelvin Daniel Sianturi Desmulyati, Desmulyati Efid Dwi Agustono Eko Pramono Eko Setia Budi Ermawati - STMIK Nusa Mandiri Jakarta, Ermawati Fadly, Raihan Abi Fajar Akbar Fattya Ariani Fattya Aryani Fitra Septia Nugraha Halid, A Abd waris Hananda Priyandaru Hasrini, Sry Hylenarti Hertyana Imam Nawawi Ino, Arnoldus Raja Irwan Pratama Aprilianto Kevin Victorio Sumolang Lesmana, Asep Muhamad Ryansyah Muhamad Tabrani, Muhamad Muhammad Rofi’i - STMIK Nusa Mandiri Jakarta Muhammad Suryanto Muhammad Tabrani Mustamin Nasya Putri Restyarna Nico Hadipratama Nopitasari Nopitasari Oky Kurniawan Priyandaru , Hananda Ratih Kurnia Richky Faizal Amir Ricky Faizal Amir Rifdan . robi sopandi Sandra Dewi Saraswati Sugiono Suhardi Suhardi Suhardi . Syah Fiqri, Muhammad Syamsul Haling, Syamsul Tabrani, Muhamad Taufik Asra Try Budi Septiandoko Unin Saputra Widya Apriliah Widya Apriliah, Widya Wijaya, Ilham Maulana Windu Gata