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All Journal Jurnal Informatika Universitas Pamulang Jurnal Sistem Komputer & Kecerdasan Buatan Infotech: Journal of Technology Information KOMMAS: Jurnal Pengabdian Kepada Masyarakat Journal of Informatics and Electronics Engineering E-Amal: Jurnal Pengabdian Kepada Masyarakat IKRAM: Jurnal Ilmu Komputer Al Muslim Jurnal Ilmu Komputer
Agung Budi Susanto
Universitas Pamulang
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions

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Analisis Prediksi Hasil Pemilu Legislatif DPR RI DKI Jakarta Tahun 2024 Menggunakan Metode Random Forest dan Gradient Boosting Effendy, Rangga Febrian; Susanto, Agung Budi; Anggai, Sajarwo
Jurnal Ilmu Komputer Vol 2 No 1 (2024): Jurnal Ilmu Komputer (Edisi Juli 2024)
Publisher : Universitas Pamulang

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Abstract

In general elections, it is closely related to predictions, predictions play an important role in obtaining results in future legislative elections. Predicting general election results can be done through a series of processes to find patterns and knowledge from a set of data using data mining techniques. To get accurate prediction results in the future, a method is needed that can be used as predictive modeling. This research aims to find out the results of model testing and predictions for the 2024 DPR RI DKI Jakarta legislative election using random forest and gradient boosting methods and to find out patterns and knowledge from the prediction results themselves. Based on the model testing results, the gradient boosting method has an accuracy value of 95.8%, precision 72.2% and recall 61.9%. Meanwhile, random forest has an accuracy value of 95.4%, precision 63.6% and recall 33.3%. The pattern and knowledge from the prediction results is that the elected legislative candidates on average are in serial numbers 1 and 2, have valid votes starting from 63,529, are male and have a doctoral degree.
Pengembangan Sistem Employee Self Service (ESS) Berbasis Web Terintegrasi Dengan Kinerja Karyawan (Studi Kasus: Astrido Group) Wibowo, Satria Ardi; Susanto, Agung Budi; Anggai, Sajarwo
Jurnal Ilmu Komputer Vol 2 No 1 (2024): Jurnal Ilmu Komputer (Edisi Juli 2024)
Publisher : Universitas Pamulang

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Abstract

Technological developments in the 4.0 era require humans to act effectively and efficiently. Astrido Group is a company operating in the automotive sector, especially car sales and services. The expected goal of this research is to build an information sistem that makes it easier for employees to update personal data, download attendance reports, process leave applications and approvals and obtain employee performance information. The sistem development method is Rapid Application Development (RAD) and modeled using the Unified Modeling Language (UML). Focus Group Discussion (FGD) Used as validation testing. The resulting software quality test is based on the four software quality characteristics of the ISO 9126 model, namely: functionality, reliability, usability and efficiency which are combined using the questionnaire method. The Black Box test results were 100%, which indicates the system was well received by users, while testing with Acunetix WVS was at Threat Level 2, which indicates the application being built is quite safe.
Evaluasi Performa Model Ensemble Learning dalam Deteksi Serangan Jaringan Internet of Things pada Dataset CIC-BCCC-IOT-HCRL-2019 Raharja, Yudi; Susanto, Agung Budi; Tukiyat, Tukiyat
Journal of Informatics and Electronics Engineering Vol 5 No 02 (2025): Desember 2025
Publisher : Unit Penelitian dan Pengabdian kepada Masyarakat Politeknik TEDC Bandung Jl. Pesantren Km 2 Cibabat Cimahi Utara – Cimahi 40513 Jawa Barat – Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70428/jiee.v5i02.1454

Abstract

Perkembangan pesat perangkat Internet of Things (IoT) membawa peningkatan kompleksitas sekaligus risiko pada keamanan jaringan. Studi ini bertujuan untuk menilai performa lima algoritma Ensemble Learning, yaitu Random Forest, AdaBoost, CatBoost, XGBoost, dan LightGBM, dalam Sistem Deteksi Intrusi (IDS) pada jaringan IoT dengan menggunakan dataset CIC-BCCC-IoT-HCRL-2019. Metode penelitian melibatkan tahap pra-pemrosesan data termasuk penerapan dua teknik normalisasi yaitu MinMaxScaler dan Normalizer, serta evaluasi model menggunakan validasi silang 5-Fold Cross-Validation dan pembagian data latih dan uji dengan rasio 80:20. Hasil eksperimen menunjukkan algoritma boosting seperti XGBoost, CatBoost, dan LightGBM secara konsisten memiliki kinerja lebih baik dibandingkan dengan model bagging tradisional seperti Random Forest. XGBoost yang dikombinasikan dengan MinMaxScaler mencapai akurasi tertinggi sebesar 0,9980, sementara LightGBM dengan MinMaxScaler mencatat waktu pelatihan tercepat yakni 2,54 detik. Temuan ini mengindikasikan bahwa penggunaan teknik boosting bersama normalisasi MinMaxScaler dapat secara signifikan meningkatkan akurasi serta efisiensi IDS berbasis IoT. Kata Kunci— Internet of Things, Deteksi Intrusi, Machine Learning, Ensemble Learning, Boosting, Normalisasi Data.
Analisis Stok Barang Menggunakan Algoritma K-Nearest Neighbor Dan Naïve Bayes Untuk Meningkatkan Efisiensi Persediaan Barang Retail Pada PT. XXX Restu Putra, Catur; Anggai, Sajarwo; Susanto, Agung Budi
Jurnal Ilmu Komputer Vol 4 No 1 (2026): Jurnal Ilmu Komputer (Edisi Januari 2026)
Publisher : Universitas Pamulang

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Efficient inventory management is a strategic necessity for retail industries that operate under highly fluctuating demand conditions. PT XXX continues to experience inaccuracies in determining stock requirements because the analysis is still carried out manually using a three-month average sales calculation. This approach is unable to capture actual warehouse variations, resulting in frequent overstock and understock conditions. This study develops a machine learning–based stock classification model using the K-Nearest Neighbor (K-NN) and Naïve Bayes algorithms by utilizing key operational warehouse variables, including average sales, ending stock, and Days of Inventory (DOI). The dataset consists of 4,324 records from November 2024 to October 2025 and was processed using Orange Data Mining. Performance evaluation was conducted using accuracy, precision, recall, and confusion matrix. The results show that K-NN achieved the best performance, with 96.80% accuracy in the prediction model and 93.00% in the test & score evaluation, outperforming Naïve Bayes, which achieved approximately 90%. The study also produced a two-level classification mapping stock status (High/Low) and warehouse recommendations (Low/Enough/Excess) which revealed a significant imbalance between High and Low categories. These findings demonstrate that machine learning–based classification methods can enhance stock assessment accuracy and support more adaptive and efficient restocking decisions in retail inventory management
Pemanfaatan Informasi BMKG untuk Peningkatan Kapasitas Mitigasi Gempa dan Tsunami Berbasis Komunitas Susanto, Agung Budi; Winarni, Winarni; Basir, Choirul
KOMMAS: Jurnal Pengabdian Kepada Masyarakat Vol. 7 No. 1 (2026): KOMMAS: JURNAL PENGABDIAN KEPADA MASYARAKAT
Publisher : KOMMAS: Jurnal Pengabdian Kepada Masyarakat

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Abstract

Indonesia merupakan wilayah rawan bencana gempa bumi dan tsunami karena berada pada pertemuan lempeng tektonik aktif. Di Jawa Barat, keberadaan Sesar Lembang berpotensi memicu gempa bumi dengan magnitudo menengah yang dapat berdampak signifikan pada wilayah padat penduduk. Sementara itu, wilayah pesisir selatan Jawa berada pada zona subduksi aktif yang berpotensi memicu gempa besar dan tsunami. Rendahnya tingkat pemahaman dan kesiapsiagaan masyarakat menjadi salah satu faktor meningkatnya risiko korban saat bencana terjadi. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan kapasitas mitigasi gempa dan tsunami berbasis komunitas melalui pemanfaatan informasi resmi dari Badan Meteorologi, Klimatologi, dan Geofisika (BMKG). Metode pelaksanaan meliputi sosialisasi, pelatihan kebencanaan, pendampingan penggunaan aplikasi informasi kebencanaan, penyusunan peta evakuasi berbasis partisipasi masyarakat, serta simulasi evakuasi mandiri. Hasil kegiatan menunjukkan adanya peningkatan pemahaman dan kesiapsiagaan masyarakat dalam menghadapi ancaman gempa dan tsunami. Pendekatan berbasis komunitas dan penggunaan sumber informasi resmi dinilai efektif dalam mendukung upaya pengurangan risiko bencana. Kata kunci: mitigasi bencana; kesiapsiagaan masyarakat; gempa bumi; tsunami; BMKG
Co-Authors Abu Khalid Rivai Abu Khalid Rivai Agustian, Rizki Andrean, Kelvin Angelina Hadriani Hadriyanto Anggai, Sajarwo Aziz, Awaludin Choirul Basir Dadang Kurnia, Dadang Edlianto, Dionisius Riyan Effendy, Rangga Febrian Hadriani, Angelina Hatonangan Samosir, Theofilus Herly Hilman, Amirul Kuntoro Kuntoro Mardiyanto Mardiyanto Nafiah, Nafiah Raharja, Yudi Restu Putra, Catur Ristasari Dwi Septiana Rohman, Fredi Muhammad Syarifudin Syarifudin Taswanda Taryo thoyyibah, Thoyyibah Tukiyat Tukiyat Tukiyat Tukiyat Tukiyat, Tukiyat Waskita, Arya Adhyaksa Wibowo, Satria Ardi Wiharjo, Sudarno Winarni Winarni