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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Dinamika Pengabdian (JDP) JTT (Jurnal Teknologi Terpadu) Paradigma International Journal of New Media Technology Jurnal ULTIMATICS Journal of Sustainable Engineering: Proceedings Series Seminar Nasional Teknologi Informasi Komunikasi dan Administrasi [SEMINASTIKA] Jurnal Ilmiah Intech : Information Technology Journal of UMUS Journal of Computer Networks, Architecture and High Performance Computing Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Teknik Informatika (JUTIF) JPM: JURNAL PENGABDIAN MASYARAKAT Jurnal Sains Indonesia DEVICE PROFICIO: Jurnal Pengabdian Masyarakat Pelita Teknologi : Jurnal Ilmiah Informatika, Arsitektur dan Lingkungan Jurnal Ilmiah SIGMA: Informatics Engineering Journal of UPB Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Pengabdian Pelitabangsa Journal of Artificial Intelligence and Digital Business Jurnal Lentera Pengabdian Proceeding International Pelita Bangsa International Journal of Educational and Life Sciences (IJELS) Jurnal Pelita Pengabdian SAINTEK
Aswan Supriyadi Sunge
Informatics Engineering Department, Faculty Of Engineering, Pelita Bangsa University, Indonesia
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ANALISIS DAN PERANCANGAN SISTEM INFORMASI MANAJEMEN INVENTARIS METODE EXTREME PROGRAMMING PADA PT. RANA GLOBAL CIBITUNG Hamdani, Hamdani; Sunge, Aswan Supriyadi; Ardiatma, Dodit
Device Vol 15 No 1 (2025): Mei
Publisher : Fakultas Teknik dan Ilmu Komputer (FASTIKOM) UNSIQ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32699/device.v15i1.9219

Abstract

PT. Rana Global Cibitung mengalami berbagai kendala dalam proses manajemen inventaris barang, terutama karena pencatatan data dan pelacakan lokasi barang masih dilakukan secara manual menggunakan spreadsheet. Proses ini tidak hanya memakan waktu, tetapi juga rawan kesalahan dan menyulitkan proses audit maupun pelacakan barang secara cepat. Keterbatasan sistem manual menyebabkan data barang sering tidak sinkron, terutama ketika terjadi perpindahan lokasi penyimpanan barang. Penelitian perancangan sistem ini bertujuan untuk merancang dan mengembangkan sistem informasi manajemen inventaris yang dapat mencatat, mengelola, dan memantau barang berdasarkan lokasi penyimpanan secara lebih akurat, efisien, dan terintegrasi. Metode dan pendekatan penelitian ini adalah Extreme Programming (XP), yang mengedepankan kolaborasi erat dengan pengguna melalui siklus pengembangan yang pendek dan iteratif. Proses listening dilakukan secara aktif untuk menggali kebutuhan riil pengguna, yang kemudian dijadikan dasar dalam perancangan fitur sistem. Hasil penelitian menunjukkan bahwa sistem yang dikembangkan mampu menyediakan berbagai fitur penting seperti pencatatan detail barang dan lokasinya, pelacakan riwayat perpindahan barang, pencarian barang berdasarkan kata kunci, serta fitur unggah gambar untuk mempermudah identifikasi. Sistem juga mendukung akses multiuser berbasis web dan memberikan kemudahan dalam pembuatan laporan. Dengan adanya sistem ini, proses inventarisasi barang di PT. Rana Global Cibitung menjadi lebih sistematis, efisien, dan dapat dipertanggungjawabkan secara administratif.
Penerapan Model MobileNetV2 Untuk Prediksi Tingkat Roasting Biji Kopi Berbasis Gambar Pada Bot Telegram Pratama, Nur; Sunge, Aswan Supriyadi; Budiarto, Eko
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 4 No. 2 (2025): Mei - Juli
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v4i2.1272

Abstract

Proses penilaian tingkat roasting biji kopi secara manual sering kali bersifat subjektif dan tidak konsisten, sehingga diperlukan sistem otomatis berbasis citra yang mampu melakukan klasifikasi secara akurat. Penelitian ini bertujuan untuk membangun model klasifikasi tingkat roasting biji kopi menggunakan arsitektur MobileNetV2 yang ringan dan efisien. Dataset terdiri dari 1.600 gambar yang dikategorikan ke dalam empat kelas: green, light, medium, dan dark. Model dilatih tanpa proses fine-tuning dan dievaluasi menggunakan metrik akurasi, loss, precision, recall, f1-score, serta confusion matrix. Hasil evaluasi pada data uji menunjukkan akurasi sebesar 98,50% dan f1-score rata-rata 0,98, menandakan performa tinggi dalam kondisi data terkontrol. Model kemudian diimplementasikan ke dalam platform bot Telegram yang memungkinkan pengguna mengirim gambar dan menerima hasil prediksi secara otomatis. Meskipun sistem menunjukkan respons cepat, pengujian terhadap gambar dari luar dataset menunjukkan penurunan akurasi hingga 45%. Hal ini mengindikasikan perlunya peningkatan kemampuan generalisasi model. Sistem ini berpotensi diterapkan sebagai alat bantu digital dalam pengawasan mutu roasting biji kopi, terutama pada skala usaha kecil dan menengah.
Kepuasan Siswa dalam Pembelajaran Interaktif Animasi 2 Dimensi Matahari Terbit Melalui Pendekatan ADDIE Sunge, Aswan Supriyadi; Pramudito, Dendy K; Prasetyo, Sutrisno Aji
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 6 No. 1 (2025): Jurnal Pengabdian kepada Masyarakat Nusantara Edisi Januari - Maret
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jpkmn.v6i1.4645

Abstract

Pembelajaran animasi berperan penting merubah cara siswa memahami mata pelajaran, terutama dalam konteks pendidikan yang menggunakan metode konvensional, seperti mendengar, mengamati dan pencatatan. Dalam menghadapi tantangan tersebut, dengan menggunakan model ADDIE memberikan pendekatan sistematis yang mengintegrasikan teori dan praktik. Melalui tahap analisis, desain, pengembangan, implementasi, dan evaluasi, siswa tidak hanya memperoleh pengetahuan teoretis, tetapi juga keterampilan praktis dalam menciptakan animasi yang menggambarkan konsep-konsep ilmiah, seperti pembelajaran mengamati tata surya. Proses ini tidak hanya meningkatkan pemahaman mereka terhadap materi, tetapi juga merangsang kreativitas dan antusiasme siswa. Hasilnya, siswa menjadi lebih terlibat dalam pembelajaran, mampu memvisualisasikan hubungan antara teori dan praktik objek di tata surya. Kepuasan siswa juga meningkat secara signifikan dan mereka merasa pembelajaran menjadi menyenangkan dan dimengerti. Maka dari itu, pembelajaran animasi tidak hanya memperkaya pengalaman pendidikan, tetapi juga mengajarkan keterampilan siswa yang relevan dengan era digital.
Interpretable Machine Learning for Employee Recruitment Prediction Using Boruta, CatBoost, Lasso, Logistic Regression, NLP, and RFE Feature Selection Sunge, Aswan Supriyadi; Suzanna, Suzanna; Mardi Putra, Hamzah Muhammad
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.4810

Abstract

Employee recruitment is one of the crucial processes in human resource management that has a direct impact on the performance and success of the company. In the digital era, the use of Machine Learning (ML) in candidate selection processes is increasingly prevalent due to its ability to enhance efficiency, accuracy, and transparency. This research is important because conventional recruitment methods often face issues such as subjective bias, slow processing times, and limitations in assessing a candidate’s true potential. ML offers a more objective, data-driven, and faster approach, enabling companies to identify the best candidates more effectively. This study aims to identify the main features that influence recruitment decisions, as well as evaluate the effectiveness and interpretability of several ML models, namely Boruta, CatBoost, Lasso Regression, Logistic Regression, Natural Language Processing (NLP), and Recursive Feature Elimination (RFE). This study uses a dataset consisting of 1,501 samples with 10 features and one class variable (0 = Not Hired, 1 = Hired). The evaluation is carried out based on the ability of each model to identify the features that make the most significant contribution to the classification results. This study has several limitations, particularly the potential bias in the data, such as demographic bias that may be reflected in historical recruitment decisions. This could lead the ML models to replicate or even reinforce such biases. Additionally, the limited dataset size may affect the models' ability to generalize to new data. In the context of this study, the main parameter used to assess the superiority of the model is the most dominant feature or the highest feature produced by each method. The test results show that the Boruta model identifies Gender as the most influential feature, while the CatBoost, Lasso Regression, Logistic Regression, and NLP models consistently place Recruitment Strategy as the most significant feature in predicting candidate eligibility. Meanwhile, the RFE model produces Distance from the Company as the highest feature that influences recruitment decisions. The uniqueness of this study lies in its approach that integrates feature interpretability models within the real-world context of recruitment decision-making. This approach not only emphasizes prediction accuracy but also promotes transparency and a clear understanding of the rationale behind each decision. It supports the development of a fairer and more accountable selection process, particularly by minimizing unconscious bias in data-driven recruitment systems. From a practical standpoint, the findings are highly relevant for human resource professionals, as the identified key features can be used to design more objective selection strategies and enhance the efficiency of candidate evaluations. Therefore, this study makes a tangible contribution to the advancement of modern, technology-based recruitment systems that prioritize fairness and decision-making efficiency. Additionally, the selection of evaluation metrics could be further elaborated to strengthen the analysis, for example by presenting the overall accuracy of each model or comparing them with alternative approaches to provide a more comprehensive view of the models' performance.
Analysis of Predicting the Number of Rejected Chips Using Random Forest at PT. Wahyu Kartumasindo Internasional Supriyadi, Agus; Sunge, Aswan Supriyadi; Tedi, Nanang
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 4 (2025): Articles Research October 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i4.7028

Abstract

Manufacturing industries face significant challenges in maintaining consistent product quality, particularly in minimizing reject rates across production machines, as high reject levels not only increase operational costs but also reduce overall efficiency and competitiveness. This study aims to develop a predictive approach using the Random Forest algorithm to forecast monthly chip rejects across different production machines, with historical reject data consisting of 1,820 records from June 2023 to September 2024 analyzed based on four primary reject categories and five production machines (DCL1, DCL2, CMI200, CMI200+, and YMJ400). The Random Forest model was applied to classify and predict reject patterns, and its performance was evaluated based on prediction accuracy and error rates, showing that the algorithm is effective in predicting reject counts with an absolute error of 0.640 ± 0.183, especially for lower reject values under 300, although accuracy decreases when handling higher reject levels above 500. Machine-level analysis further reveals that DCL1 and DCL2 consistently contribute the highest reject counts with high variability, while CMI200 and CMI200+ demonstrate stable performance with most rejects below 300, and YMJ400 generally records lower rejects but occasionally exhibits spikes, suggesting inconsistent performance. In conclusion, the Random Forest model provides a reliable predictive framework for monitoring reject trends, identifying DCL1 and DCL2 as priority targets for improvement, and supporting proactive maintenance strategies to enhance overall production quality.
Prediksi Sentiment Analysis Dalam Membahas Produk Di E-Commerce Dengan Algoritma Naive Bayes Kurniasih, Nabila; Sunge, Aswan Supriyadi; Amali, Amali; Priyo, Basuki Edi; Trialfhianty, Tyas Ismi
Jurnal SIGMA Vol 15 No 3 (2024): Desember 2024
Publisher : Teknik Informatika, Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/sigma.v15i3.6043

Abstract

Analisis sentimen merupakan proses mengubah data kualitatif menjadi data kuantitatif dengan menghilangkan kata dan simbol yang tidak relevan, guna mengidentifikasi opini positif dan negatif dalam ulasan pengguna. Penelitian ini bertujuan untuk mengklasifikasikan sentimen pada ulasan pengguna aplikasi Shopee menggunakan metode Naive Bayes. Data diperoleh dari dataset sekunder melalui GitHub dan diolah menggunakan algoritma Term Frequency-Inverse Document Frequency (TF-IDF) untuk ekstraksi fitur. Evaluasi model dilakukan menggunakan confusion matrix untuk mengukur akurasi, presisi, recall, dan F1-Score. Hasil penelitian menunjukkan bahwa mayoritas ulasan bersentimen positif, dengan akurasi model sebesar 84%, presisi negatif 89% dan positif 81%, recall negatif 76% dan positif 91%, serta F1-Score negatif 82% dan positif 85%, sehingga membuktikan bahwa metode Naive Bayes efektif dalam klasifikasi sentimen ulasan e-commerce.
Kepuasan Pelanggan Terhadap Implementasi Sistem Absensi Di Perusahaan Graha Digital Network Dengan Algoritma C4.5 Anggara, Bangkit Akbar; Sunge, Aswan Supriyadi Sunge; Sari, Putri Anggun; Mardi Putra, Hamzah Muhammad; Wulandari, Anna
Jurnal SIGMA Vol 15 No 3 (2024): Desember 2024
Publisher : Teknik Informatika, Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/sigma.v15i3.6046

Abstract

Kemajuan teknologi informasi telah memungkinkan bisnis untuk menggantikan proses manual, seperti pencatatan kehadiran karyawan, dengan sistem digital. Graha Digital Network, untuk meningkatkan efisiensi dan akurasi pencatatan kehadiran, telah mengimplementasikan sistem absensi digital. Penelitian ini bertujuan untuk mengevaluasi kepuasan pengguna terhadap sistem tersebut dan mengidentifikasi elemen yang mempengaruhinya dengan menggunakan algoritma C4.5. Metode kuantitatif digunakan dengan pengumpulan data melalui kuesioner yang mengukur fitur-fitur seperti kemudahan penggunaan, keandalan, dan dampaknya terhadap produktivitas kerja. Hasil analisis menunjukkan bahwa fitur nomor 3 memiliki nilai gain tertinggi, menunjukkan informasi paling signifikan dalam sistem absensi, sementara fitur nomor 8 memiliki pengaruh paling rendah. Pohon keputusan yang dihasilkan menggambarkan kontribusi masing-masing aspek terhadap kepuasan pengguna. Penelitian ini menemukan bahwa beberapa fitur sistem absensi memiliki pengaruh signifikan terhadap kepuasan pengguna, memberikan manajemen Graha Digital Network kesimpulan dan rekomendasi untuk meningkatkan fitur tertentu dan mengoptimalkan sistem demi meningkatkan kepuasan pengguna dan produktivitas.
Using Graph Neural Networks and CatBoost for Internet Security Prediction with SMOTE Sunge, Aswan Supriyadi; Hendric, Spits Warnars Harco Leslie; Pramudito, Dendy K.
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 4 (2024): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i4.30157

Abstract

Internet security is the most important issue in cyberspace, on the other hand, cybercrime occurs, and the most serious threat is the theft of personal data and its misuse for the benefit of others. Although cyberspace is while internet security cannot eliminate all risks, predictive models can significantly reduce cybercrime by identifying vulnerabilities if you know how to prevent it. One of the most important things is that many internet users do not know what measures are used to avoid and whether it is safe to visit or explore, on the other hand, in system development existing studies on internet security prediction often rely on generic models that lack precision in identifying influential features or ensuring class balance in developing internet security. In this case, Deep Learning (DL) helps learn patterns from recorded data, find relevant patterns, and use the model effectively. The purpose of this study is to identify the most influential features in internet security and evaluate the effectiveness of advanced machine learning models, such as Graph Neural Networks (GNNs) and Categorical Boosting (CatBoost), for predicting internet safety. So far other studies have tested the entire data set and used a model that is generally. This is expected to lead to the design or development of systems and programs that are useful for internet security. The study used a dataset of 11,055 records with 30 features and binary classification labels ('Safe' and 'Not Safe'). To address the class imbalance, SMOTE was applied before splitting the data into training and testing sets. In testing the Graph Neural Networks (GNNs) model achieved 93.58% accuracy, 93.63% precision, 93.58% recall, and 93.55% F1-score, demonstrating its effectiveness for internet security prediction. From the results of testing the CatBoost model was used to identify key features, revealing that the 'URL of Anchor,' 'SSLFinal State,' and 'Web Traffic' have the most significant impact. From the experiments conducted, the CatBoost effectively identified features with the highest on prediction accuracy, and the GNNs model is very accurate and precise for developing applications or systems to predict internet security.
The Use of K-Means Algorithm Clustering in Grouping Life Expectancy (Case Study: Provinces in Indonesia) Nugraha, Dimas Reza; Zy, Ahmad Turmudi; Sunge, Aswan Supriyadi
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 3 (2024): Articles Research Volume 6 Issue 3, July 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i3.4171

Abstract

Life expectancy is defined as information that illustrates the age of the death of a population. Life expectancy is a general picture of the state of a region. If the infant mortality rate is high, then the life expectancy in the area is low. And vice versa, if the infant mortality rate is low, the life expectancy in the region is high. Life expectancy is also a benchmark for government actions in improving the welfare of society and the human development index. For this reason, it is necessary to group life expectancy data to make it easier to determine the provinces with high, middle, and low life expectancy. The results of cluster testing using the silhouette score method showed that two subjects had a low silhouette score level, which caused the cluster value to be less than optimal, namely East Java  & Gorontalo. The clustering results found that the cluster was divided into 3, namely cluster 1, with a high level of life expectancy consisting of 10 provinces, namely East Java, Riau, North Sulawesi, Bali, North Kalimantan, DKI Jakarta, West Java, Central Java, East Kalimantan and Special Region of Yogyakarta. Cluster 2 has a level of middle-life expectancy consisting of 18 provinces, namely Gorontalo, North Maluku, Central Sulawesi, South Kalimantan, North Sumatra, Bengkulu, West Sumatra, Central Kalimantan, Aceh, South Sumatra, Banten, Kep. Riau, South Sulawesi, Kep. Bangka Belitung, Lampung, West Kalimantan, Southeast Sulawesi and Jambi. Cluster 3, with a low level of life expectancy, consists of 6 provinces, namely West Sulawesi, Papua, Maluku, West Papua, West Nusa Tenggara, and East Nusa Tenggara.
Comparative Analysis of Earthquake Prediction with SVM, Naïve Bayes, and K-Means Models: Comparative Analysis of Earthquake Prediction with SVM, Naïve Bayes, and K-Means Models Muttaqin, Ahmad Fadhiil; Sunge, Aswan Supriyadi; Zy, Ahmad Turmudi
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 1 (2025): Article Research January 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i1.5085

Abstract

Earthquakes are natural disasters with significant impacts on people and the environment, so effective methods for prediction are needed to improve preparedness and risk mitigation. This study analyzes the performance of three algorithms Support Vector Machine (SVM), Naïve Bayes, and K-Means in predicting earthquakes in Indonesia using a dataset containing 4,645 historical data from BMKG processed through preprocessing, data separation, analysis, and performance evaluation with RapidMiner tools. The results show that SVM has the best performance with 99.87% accuracy, 99.83% precision, and 95.61% recall, making it highly relevant for earthquake prediction. Naïve Bayes achieved 90.31% accuracy and 95.08% recall, but the low precision (57.24%) shows the limitations of this model. K-Means successfully clusters earthquakes into two categories: small (3,661 data) and large (55 data) earthquakes, with a Davies-Bouldin Index value of 0.579, reflecting good clustering quality. Based on these results, SVM is recommended as a superior earthquake prediction model, while Naïve Bayes and K-Means are more suitable for additional analysis. This approach confirms the potential of machine learning algorithms in supporting future earthquake risk mitigation.
Co-Authors Abdul Halim Anshor Afriantoro, Irfan Agung Nugroho Agus Supriyadi Agus Suwarno Ahmad Turmudi Zy Aji, Prasetyo Wicaksono Amali, Amali Anggara, Bangkit Akbar Annisa Maulana Majid Anshor, Abdulhalim Antika Zahrotul Kamalia Aprilia Nur Sa'adah Arief Nugraha Arrafi Jehansyah Sobari Al'Ayubi Budiarto, Eko Christa Pratiwi Deas Aghellar Dendy K. Pramudito Dendy Pramudito Dodit Ardiatma Edora Edora Edora, Edora Edy Widodo Erdi Etty Zuliawati Zed Fatchan , Muhammad Fauziah , Sifa Febyan Sulastri Fitri Rezeki Hamdani Hamdani Hamzah Muhammad Mardi Putra Juan Fakhri Khalis Sofi Kurniasih, Nabila Listian Indriyani Achmad Miftahu Rizkiyah Miftahuljannah Muttaqin, Ahmad Fadhiil Nanang Susanto Nanang Tedi Kurniadi Nawangsih, Ismasari Naya, Candra Nugraha, Dimas Reza Pramudito, Dendy K. Pramudito, Dendy K. Pramudito Prasetyo, Sutrisno Aji Prasetyo, Sutrisno Aji Prasetyo Pratama, Nur Priyo, Basuki Edi R. R. Wening Ken Widodasih Riady, Sasmitoh Rahmad Rismawati Sari, Putri Anggun Septiawan Dwi Pramukti Suherman Suprapto Suprapto Suprapto Suprapto Sutrisno Aji Prasetyo Suzanna, Suzanna Tasya Yustira Tedi Kurniadi, Nanang Tedi, Nanang Tri Ngudi Wiyanto Tri Ngudi Wiyatno Tyas Ismi Trialfhianty Warnars, Harco Leslie Hendric Spits Wulandari, Anna Zahrotul Kamalia, Antika Zy, Ahmad Turmudi