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All Journal JSAI (Journal Scientific and Applied Informatics) Journal of Information Systems and Informatics Jurnal Sisfotek Global Arcitech: Journal of Computer Science and Artificial Intelligence Jurnal Pengabdian Masyarakat Tekno
Burham Isnanto
Institut Sains dan Bisnis Atma Luhur
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering

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Selection of the Best Computer Laboratory Assistant Using AHP and Smart Methods Ari Amir Alkodri; Supardi Supardi; Burham Isnanto; Kiswanto Kiswanto
JURNAL SISFOTEK GLOBAL Vol 12, No 1 (2022): JURNAL SISFOTEK GLOBAL
Publisher : Institut Teknologi dan Bisnis Bina Sarana Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38101/sisfotek.v12i1.451

Abstract

Computer Laboratory Assistant, abbreviated as ASLAB, includes a Lab Assistant in a Computer Laboratory. ASLAB helps improve service performance in the Computer Laboratory, such as assisting Lecturers when teaching practicum at the Computer Laboratory and assisting in installing software on Student laptops so that the performance of Computer Laboratory services is always good. Problems The Head of the Computer Laboratory has difficulty assessing the comparison of ASLAB with other ASLABs to determine the best or not good performance. The calculation of the final value uses the mean value method so that all criteria are considered to be of equal importance, so we cannot know which criterion is more important. The research objective is to apply the AHP and SMART methods for selecting the best Computer Laboratory Assistant so that it can determine the criteria and ranking weights for the best Computer Laboratory Assistant so that it can be used as a basis for decision making and applied by the Head of the Computer Laboratory in selecting the best Computer Laboratory Assistant.
Mobile Applications Quick Response Reports For Successful Practices In Computer Laboratory Ari Amir Alkodri; Supardi Supardi; Burham Isnanto; Seno Hadi Saputro; Agus Dendi Rachmatsyah; Gita Aditia
JURNAL SISFOTEK GLOBAL Vol 11, No 2 (2021): JURNAL SISFOTEK GLOBAL
Publisher : Institut Teknologi dan Bisnis Bina Sarana Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (749.013 KB) | DOI: 10.38101/sisfotek.v11i2.397

Abstract

With the rapid advancement of information technology today, every world of education requires campuses to develop teaching and learning services in their special practicum in the laboratory. Rapid response laboratory services are a form of participation to take part in the implementation of practicum adjusting standard operating procedures (SOP). In this case, the author takes a case study at the Atma Luhur ISB computer laboratory, making a damage report or PC troubleshooting now still in the form of LOG books and manuals, and the processing time is waiting for lecture hours to finish. Thus, a rapid response application process for response and information service laboratories is needed to make it easier for laboratory workers to carry out job descriptions. The method used in designing this rapid response system application is the waterfall model and is run on the Android operating system. The results of the rapid response application in the Atma Luhur ISB laboratory can help smooth practicum in this laboratory, making it easier for the laboratory and to obtain service responses and practicum services more quickly and effectively. This application after being tested for functional aspects by distributing questionnaires to laboratory assistants got an average value of 87.083% (good criteria), so overall it can be concluded that this application can be applied in the Atma Luhur ISB laboratory.
Teknik Steganografi Penyembunyian Pesan Text Rahasia Pada Citra Digital Dengan Metode Least Significant Bit Supardi Supardi; Ari Amir Alkodri; Burham Isnanto
JURNAL SISFOTEK GLOBAL Vol 11, No 1 (2021): JURNAL SISFOTEK GLOBAL
Publisher : Institut Teknologi dan Bisnis Bina Sarana Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (460.159 KB) | DOI: 10.38101/sisfotek.v11i1.351

Abstract

Steganografi adalah salah satu teknik yang paling kuat untuk menyembunyikan keberadaan data rahasia tersembunyi di dalam penutup obyek. Gambar adalah objek penutup paling populer untuk Steganografi dan dalam steganografi gambar kerja ini diadopsi. Menanamkan informasi rahasia di dalam gambar membutuhkan perhitungan yang intensif, dan oleh karena itu, merancang Steganography dalam mempercepat kecepatan perangkat Steganografi. Ini diimplementasikan menggunakan prosesor ARM7TDMI dan GSM 900. Ada beberapa teknik untuk menyembunyikan informasi di dalamnya gambar sampul. Teknik domain spasial memanipulasi nilai bit pixel gambar sampul untuk menanamkan informasi rahasia. Bit rahasia ditulis langsung ke sampul image pixel bytes. Akibatnya, teknik domain spasial sederhana dan mudah diterapkan. The Least Significant Bit (LSB) adalah salah satu techniques utama dalam steganografi citra domain spasial. Dalam penelitian ini, teknik baru steganografi LSB telah diusulkan yang merupakan versi improvisasi teknik LSB satu bit. Ada pun tujuan dari penelitian untuk menyembunyikan pesan teks rahasia kedalam file gambar sehingga pesan rahasia menjadi aman karena sudah disimpan didalam file gambar sehingga menjadikan alternatif pengiriman pesan agar terhindar dari pencurian dan sabotase.
Implementasi Monitoring Prakerin Programming Kompetensi Keahlian Jurusan Teknik Komputer Ari Amir Alkodri; Burham Isnanto; Supardi Supardi
JURNAL SISFOTEK GLOBAL Vol 11, No 1 (2021): JURNAL SISFOTEK GLOBAL
Publisher : Institut Teknologi dan Bisnis Bina Sarana Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1117.311 KB) | DOI: 10.38101/sisfotek.v11i1.349

Abstract

Peningkatan kualitas siswa praktik kerja industri dibidang komputer yang siap terjun ke lapangan kerja merupakan salah satu mutu dari setiap SMK, salah satunya beberapa Sekolah Menengah Kejuruan (SMK) baik yang Negeri ataupun Swasta selalu mengirimkan siswa nya untuk prakerin (Praktek Kerja Industri) Pada sebuah perusahaan atau institusi selama 3 bulan dengan kerja lapangan dan menghasilkan laporan. Penelitian ini akan merancang aplikasi android prakerin menggunakan monitoring prakerin programming dengan pemantau aktifitas kegiatan siswa terhadap pembimbing DUDI yang akan diketahui informasi oleh guru pembimbing / pihak sekolah. Proses pengujian aplikasi menggunakan ISO 9126 dengan stakeholder dan pengujian efektivitas, efisiensi, usefullness untuk memastikan aplikasi bisa dipakai dan sesuai fungsional oleh sekolah (SMK Kejuruan). Hasil penelitian berupa monitoring prakerin programming yang dapat berdampak pada pemanfaatan bagi guru pembimbing akan laporan kegiatan prakerin. Aplikasi ini setelah diuji untuk aspek kegunaan bernilai 84,35 (kriteria baik), aspek efisiensi bernilai 83,06 (kriteria baik), aspek fungsional mendapatkan nilai 83,05  (kriteria baik), dan aspek reliability 80,96 (kriteria baik), sehingga secara keseluruhan rata-rata 83,05 (kriteria baik).
Pelatihan Web Desain Bagi Pegawai Dalam Upaya Pengembangan Kompetensi SDM Supardi; Elly Yanuarti; Ari Amir Alkodri; Burham Isnanto; Kiswanto
Jurnal Pengabdian kepada Masyarakat TEKNO (JAM-TEKNO) Vol 2 No 1 (2021): Juni 2021
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Sumber daya manusia merupakan individu yang bekerja sebagai penggerak suatu organisasi atau perusahaan yang berfungsi sebagai aset yang harus dilatih dan dikembangkan kemampuannya. Salah satu intansi yang saat ini berupaya melakukan pengembangan kemampuan SDM adalah UPT Sistem Informasi Polman Babel. Upaya ini dilaksanakan melalui jalur kerjasama dalam program pelatihan berbasis kompetensi. Dari hasil wawancara dengan narasumber dari mitra pengabdian kepada masyarakat ini menyatakan bahwa ada beberapa karyawan yang ingin diupayakan oleh mitra agar kesejahteraannya dapat meningkat yaitu dengan pengadaan Pegawai Pemerintah dengan Perjanjian Kerja (PPPK). Pegawai Pemerintah dengan Perjanjian Kerja (PPPK) merupakan warga negara yang diangkat berdasarkan perjanjian kerja untuk jangka waktu tertentu dalam rangka melaksanakan tugas pemerintahan. Metode yang kami gunakan pada kegiatan pelatihan ini adalah menggunakan pendekatan teori, praktek dan evaluasi. Metode penyampaian materi dilakukan secara daring/online dan tatap muka/offline. Penyampaian teori dan praktikum dilakukan dengan tatap muka dan daring dengan total selama 5 hari. Metode penyampaian praktikum dilakukan secara daring dengan menjelaskan teori dan tutorial prakteknya kemudian memberikan tugas/latihan bagi peserta. Dalam tahap evaluasi didapatkan hasil secara umum semua peserta dapat mencapai KKM baik dari penguasaan teori maupun keterampilan praktik. Jadi persentase peserta yang mencapai KKM adalah 90% dan persentase peserta yang menyelesaikan program pelatihan ini adalah 100%. Berdasarkan hasil proses pelaksanaan kegiatan pelatihan, secara umum pelatihan ini berjalan dengan lancar. Adanya peningkatan keterampilan dan pengetahuan dari peserta dapat dipastikan bahwa akan terjadi peningkatan kinerja dalam pelaksanaan tugasnya sehari-hari.
Komparasi Algoritma Klasifikasi Performa Akademik Mahasiswa Bisnis Digital: SVM, Random Forest, XGBoost, dan LightGBM dengan Penanganan Class Imbalance Menggunakan SMOTE Lili Indah Sari; Burham Isnanto; Wishnu Aribowo Probonegoro
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 2 (2026): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i2.10535

Abstract

This study aims to compare the performance of classification algorithms, namely Support Vector Machine (SVM), Random Forest, XGBoost, and LightGBM, in predicting the academic performance of Digital Business students at ISB Atma Luhur by handling class imbalance using the Synthetic Minority Oversampling Technique (SMOTE). The dataset consisted of 326 student records with 55 questionnaire-based Likert-scale features, GPA, and semester data classified into two academic performance classes. The research stages included data preprocessing, normalization, SMOTE implementation, feature selection using feature importance, model training, and evaluation using accuracy, precision, recall, F1-score, F1 Macro, AUC-ROC, and training time metrics. The results showed that the XGBoost algorithm achieved the best performance with an accuracy of 0.8621, an F1 Macro score of 0.85, and an AUC value of 0.91. LightGBM produced performance close to XGBoost while providing faster training time. The implementation of SMOTE successfully improved minority class classification performance across all algorithms, particularly in terms of F1-score. The findings indicate that the combination of boosting algorithms and class imbalance handling techniques is effective for machine learning-based academic performance prediction systems.
Random Forest-Based Poverty Forecasting Using Socioeconomic Indicators in Bangka Belitung Islands Province Burham Isnanto; Rahmat Sulaiman
Arcitech: Journal of Computer Science and Artificial Intelligence Vol. 6 No. 1 (2026): June 2026
Publisher : Institut Agama Islam Negeri (IAIN) Curup

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29240/arcitech.v6i1.17171

Abstract

Poverty remains a significant socioeconomic challenge in the Bangka Belitung Islands Province, Indonesia, where economic dependence on tin mining and plantation commodities creates structural vulnerabilities that influence regional welfare conditions. Poverty remains a significant socioeconomic challenge in the Bangka Belitung Islands Province, Indonesia, where economic dependence on tin mining and plantation commodities creates structural vulnerabilities that influence regional welfare conditions. Previous poverty forecasting studies in Indonesia have predominantly employed statistical and econometric models, which are often limited in modeling non-linear socioeconomic interactions and are rarely validated using subnational panel data. Consequently, the potential of machine learning techniques, particularly Random Forest, for poverty prediction at the regency and municipal level remains underexplored. This study addresses this gap by developing a Random Forest-based poverty prediction model using socioeconomic indicators from 2019–2025. This study proposes a machine learning approach to predict poverty rates using the Random Forest algorithm implemented in Altair AI Studio (RapidMiner). Panel data covering the period 2019–2025 were collected from official publications of Badan Pusat Statistik (BPS) Bangka Belitung Islands Province. Three socioeconomic indicators were used as predictor variables: the Human Development Index (HDI), Open Unemployment Rate (OUR), and the number of poor people in each regency or municipality. The dataset consists of 49 observations representing seven administrative regions across seven years. The developed Random Forest model achieved an R² value of 0.800, an RMSE of 0.722, and an MAE of 0.561, demonstrating good predictive accuracy. The validated model was subsequently used to estimate poverty rates for 2026, producing predictions ranging from 2.762% to 6.244%. These findings highlight the potential of machine learning techniques to support poverty forecasting and evidence-based regional development policies.
An Empirical Comparison of C4.5, Naive Bayes, and KNN for Scholarship Selection Burham Isnanto; Rahmat Sulaiman
Journal of Information System and Informatics Vol 8 No 3 (2026): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i3.1617

Abstract

Scholarship selection is a critical process in higher education that requires objective, fair, and efficient evaluation of applicants based on academic and socio-economic criteria. However, manual assessment methods are often vulnerable to bias, inconsistency, and administrative inefficiencies, which may affect the transparency and quality of decision-making. This study compares the performance of three supervised machine learning algorithms—C4.5 Decision Tree, Naive Bayes, and K-Nearest Neighbor (KNN)—for scholarship recipient classification. The dataset consisted of 1,500 student records obtained from the KelasAI repository and included ten predictor attributes, namely Grade Point Average, Parental Income, Academic Semester, Family Dependents, Organizational Involvement, Academic Achievement, Regional Origin, Scholarship Type, National Examination Score, and Economic Status. The target variable was categorized into Accepted and Rejected classes. Experiments were conducted using RapidMiner Studio with 10-fold stratified cross-validation to ensure reliable model evaluation. The results showed that Naive Bayes achieved the best performance, with 81.6% accuracy, 81.8% precision, and 81.3% recall, outperforming C4.5 and KNN. These findings demonstrate the potential of machine learning to support more transparent and data-driven scholarship selection processes.
Co-Authors Agus Dendi Rachmatsyah Ari Amir Alkodri Elly Yanuarti Gita Aditia Kiswanto Lili Indah Sari Rahmat Sulaiman Seno Hadi Saputro Supardi Supardi Wishnu Aribowo Probonegoro