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UUNG UNGKAWA
INSTITUT TEKNOLOGI NASIONAL BANDUNG
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Health Professions Law, Crime, Criminology & Criminal Justice Library & Information Science Public Health

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Perbandingan Metode Multifactor Evaluation Process dan Analytical Hierarchy Process (Studi Kasus: Promosi Jabatan di Biro Kepegawaian Institut Teknologi Nasional Bandung) UUNG UNGKAWA; RIDHO FARUQI; NUR FITRIANTI F
MIND (Multimedia Artificial Intelligent Networking Database) Journal Vol 4, No 2 (2019): MIND Journal
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/mindjournal.v4i2.32-41

Abstract

AbstrakDalam sistem pendukung keputusan, banyak dijumpai metode untuk mengevaluasipilihan dalam mengambil keputusan. Dua di antaranya adalah metode MultifactorEvaluation Process (MFEP) dan Analytical Hierarchy Process (AHP). Dalam paperini disajikan perbandingan keduanya baik secara prosedural maupun hasil akhirdalam pemberian peringkat karyawan di Biro Kepegawaian Institut TeknologiNasional (Itenas) Bandung. Hasil menunjukkan, bahwa metode AHP lebih komplekdibanding MFEP sedangkan hasil pemeringkatan diperoleh perbedaan di peringkatempat dan lima dari lima karyawan yang dievaluasi.Kata kunci: Multifactor Evaluation Process, Analytical Hierarchy Process, sistempendukung keputusan. AbstractThere are many methods in the decision support system to evaluate candidates toselect the best one of them to promote to a position. Two of them are MultifactorEvaluation Process (MFEP) and Analytical Hierarchy Process (AHP). This workcompares the two methods both in the procedural point of view of how the decisionis made and in the final result of evaluation in the ranking of the employees of BiroKepegawaian Institut Teknologi Nasional (Itenas) Bandung. The process ofcalculation shows that AHP more complicated than MFEP, but the rangking of thetwo methods show similar result except in the 4th and 5th rank between fiveemployees.Keywords: Multifactor Evaluation Process, Analytical Hierarchy Process, decisionsupport system.
Perancangan Aplikasi Mobile Pajak Daerah Di Kabupaten Ciamis Uung Ungkawa; Marisa Premitasari; Muhammad Daffa Fathur Rahman; Rangga Alfyansyah; Dhiki Romadinur
Jurnal Pelayanan Hubungan Masyarakat Vol. 1 No. 3 (2023): September : Jurnal Pelayanan Hubungan Masyarakat
Publisher : Universitas Katolik Widya Karya Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59581/jphm-widyakarya.v1i3.1167

Abstract

This research aims to design an Android-based local tax service information system at the Regional Financial Management Agency of Ciamis District. The system will facilitate access and processing of tax information through features such as online tax payment, tax registration, exemption request submission, and tax compliance reporting. The implementation of this system is expected to enhance the effectiveness and efficiency of tax services, as well as improve tax awareness and compliance in the community of Ciamis District.
Metoda Kalibrasi untuk Sistem Geofencing dengan Poligon Tertutup Marisa Premitasari; Uung Ungkawa; Petrick Juliansz Kakalang
Rekayasa Hijau : Jurnal Teknologi Ramah Lingkungan Vol 7, No 2 (2023)
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/jrh.v7i2.112-122

Abstract

ABSTRAKKalibrasi dari pengukuran suatu alat yaitu modul Global Positioning System (GPS) Neo M8M untuk mendapatkaan data posisi keberadaan dari suatu objek. Data yang diambil berupa data live latitude dan longitude dari objek yang dipasang modul tersebut. Sepeda motor sebagai uji coba objek dimana sepeda motor bergerak di dalam lingkungan kampus ITENAS Bandung. Pergerakan objek dibatasi oleh pembatas digital berupa sebuah bangun polygon segi sepuluh. Bila objek berada diluar area polygon , maka sistem akan memberi notifikasi bahwa objek berada diluar area dan begitu juga sebaliknya. Nilai posisi dari sepeda motor dianggap akurat, bila kalibrasi sudah dilakukan dititik-titik yang telah ditetapkan. Titik-titik tersebut yaitu titik ITN 43, ITN 39, ITN 27, ITN 28 dan ITN 14. Pada pagi hari terdapat error akurasi sebesar 0,61%, pada siang error terhitung sekitar 0,85%, pada sore hari terjadi error sekitar 0,82% dan malam, ada kesalahan sekitar 0,61%.. Nilai keakuratan posisi GPS sebagai masukan untuk sistem geofencing dengan sepuluh sudut polygon tertutup (decagon) agar sepeda motor dapat terdeteksi didalam atau di luar area.Kata kunci: Kalibrasi, GPS, Objek bergerak, closed polygon, GeofencingABSTRACTCalibration was carried out from a Global Positioning System (GPS) ‘s device, namely the Neo M8M Global Positioning System (GPS) module to obtain the data of real object position.. The data has taken from moving object that was installed by the module , yet latitude and longitude object’s position at real time .Motorbikes as a test object whereas it was moving around in the ITENAS Bandung campus area. The object movement area was limited by a decagon’s digital boundary. If the object’s position was outside the the-sided polygon area, then the system will give a notification that the position detected outside and vice versa. The latitude and longitude value considered as accurate values if the calibration has been carried out at five predetermined points, that was ITN 43, ITN 39, ITN 27, ITN 28 and ITN 14. in the morning there was accuracy errors of 0.61%, in the afternoon the error was counted around 0.85%, in the afternoon there was an error of about 0.82% and at night, an error generated about 0.61%. The accuracy of the GPS position as input for a geofencing system with ten closed polygon corners (decagon) so that the motorbike will detected inside or outside the area.Keywords: Calibration, GPS, Moving object , closed polygon, geofencing
SISTEM ABSENSI DAN REKAPITULASI DI SMP NEGERI 1 BATUJAJAR Nirmala Putri Ismail; Alif Abdul Hakim; Tegar Subagdja; Uung Ungkawa
Community Development Journal : Jurnal Pengabdian Masyarakat Vol. 6 No. 1 (2025): Volume 6 No. 1 Tahun 2025
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/cdj.v6i1.41913

Abstract

Sistem absensi siswa yang efisien dan akurat merupakan kebutuhan penting bagi institusi pendidikan, termasuk SMP Negeri 1 Batujajar. Saat ini, sekolah masih menggunakan metode absensi manual yang memakan waktu, rentan terhadap kesalahan pencatatan, dan sulit dalam pelaporan serta pemantauan real-time. Studi ini bertujuan untuk mengatasi permasalahan tersebut dengan mengembangkan sistem absensi digital berbasis teknologi.  Tahapan yang dilakukan meliputi analisis kebutuhan melalui wawancara dan observasi, perancangan sistem menggunakan Entity Relationship Diagram (ERD) dan Table Relationship Diagram (TRD), serta pengembangan sistem berbasis framework Laravel dengan database terpusat. Sistem ini memungkinkan pencatatan kehadiran otomatis, pemantauan kehadiran siswa secara real-time, serta pembuatan laporan absensi secara cepat dan akurat. Implementasi melibatkan pelatihan bagi guru dan staf administrasi untuk memastikan adopsi sistem berjalan optimal. Evaluasi dilakukan untuk mengukur efektivitas sistem sekaligus mengumpulkan umpan balik guna penyempurnaan. Hasil implementasi menunjukkan bahwa sistem absensi digital ini mampu meningkatkan efisiensi pengelolaan absensi, mengurangi kesalahan pencatatan, dan mempermudah pelaporan data kehadiran. Dengan demikian, sistem ini tidak hanya mendukung modernisasi administrasi sekolah, tetapi juga menciptakan transparansi dan efisiensi yang lebih baik dalam mendukung proses belajar mengajar di sekolah.
Penerapan ResNeXt dan Long Short-Term Memory untuk Deteksi Video Deepfake Chalifa Chazar; Firhan Hafiansyah; Milda Gustiana Husada; Uung Ungkawa; Rizka Milandga Milenio
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 11 No. 2 (2026): May 2026
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.6085

Abstract

Deepfake is a form of facial manipulation in videos that utilizes artificial intelligence-based models to generate highly realistic visual content. The increasing spread of Deepfake has the potential to cause misinformation, manipulate public opinion, and enable the misuse of digital content, making reliable detection systems increasingly necessary. This study develops a face-based Deepfake video detection system by combining spatial and temporal analysis within a unified processing framework. ResNeXt is employed to extract visual facial characteristics from each frame, while Long Short-Term Memory (LSTM) is utilized to learn facial pattern changes across frames in a video sequence. The dataset used is sourced from FaceForensics++, consisting of 1000 original videos and 1000 Deepfake videos. All data are processed through frame extraction and face detection stages. Performance evaluation is conducted using accuracy, precision, recall, and F1-score metrics. The experimental results show that ResNeXt, as the baseline model, achieves 77.67% accuracy and 76.66% F1-score, while integrating LSTM improves system performance to 81.00% accuracy and 80.41% F1-score. These findings indicate that the utilization of temporal information contributes to improved stability and accuracy in face-based Deepfake video detection.
Penerapan Convolutional Block Attention Module untuk Meningkatkan Klasifikasi Glaukoma Menggunakan ResNeXt Chalifa Chazar; Arib Arkaan Fadhlullah; Milda Gutiana Husada; Uung Ungkawa
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 11 No. 3 (2026): September 2026
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.6084

Abstract

Glaucoma is a chronic ocular disease characterized by progressive optic nerve damage and is one of the leading causes of irreversible blindness. Manual analysis of retinal fundus images is time-consuming, particularly under high clinical workloads. To improve the efficiency of clinical diagnostic workflows, this study aims to develop a glaucoma classification system using a hybrid deep learning model that integrates the ResNeXt architecture with a Convolutional Block Attention Module (CBAM). In the proposed approach, ResNeXt is employed to capture complex feature representations, while CBAM enhances feature extraction by directing the model’s attention to more informative spatial and channel-wise features. Performance evaluation demonstrates that the proposed model outperforms the baseline architecture. The standard ResNeXt model achieves an accuracy of 91.61%, an F1-score of 88.98%, and an AUC of 93.1%. In contrast, the ResNeXt model integrated with CBAM shows improved performance, attaining an accuracy of 92.70%, an F1-score of 90.60%, and an AUC of 95,6%. These results indicate that the integration of CBAM effectively enhances the sensitivity and robustness of the glaucoma classification model compared to the standard architecture.
Implementation of RAG in Jambi Cultural Information Question Answering System Using Qwen2.5-7B Devit Saputra; Uung Ungkawa
Journal of Applied Informatics and Computing Vol. 10 No. 5 (2026): October 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i5.14127

Abstract

Large Language Models (LLMs) are inherently closed-book, making them prone to hallucination when answering domain-specific queries with limited training data, such as local culture. Information on Jambi's cultural heritage remains scattered across various sources, causing inefficient integrated retrieval. This study implements a Retrieval-Augmented Generation (RAG) architecture using the open-source LLM Qwen2.5-7B, strengthened by Hybrid Search (BM25 and dense retrieval fused via Reciprocal Rank Fusion), BGE-Reranker-Large reranking, and a Confidence Scorer. Three retrieval configurations Dense RAG, BM25 RAG, and Hybrid RAG were evaluated end-to-end using 130 In-Domain (ID) and 50 Out-of-Context (OOC) test queries, measured through Precision@10, Recall@10, Final Context Precision/Recall, BLEU-1, BLEU-4, ROUGE-L, Groundedness, and Rejection metrics (FRR/TRR). Results show that Dense RAG performed most dominantly end-to-end, achieving the highest Pure Recall@10 (0.992), BLEU-1 (0.499), ROUGE-L (0.506), Groundedness (0.611), and lowest False Rejection Rate (11.5%). BM25 RAG showed a specific advantage in Final Context Precision (0.746) after reranking. Hybrid RAG offered a competitive performance compromise, with Final Recall (0.938) and BLEU-4 (0.288) on par with Dense, alongside absolute hallucination protection (100% True Rejection Rate) on OOC queries. These findings indicate that while Hybrid fusion and the Confidence Scorer effectively mitigate hallucination, pure Dense retrieval consistently provides the most robust context support on the tested Jambi Culture dataset. These results demonstrate that systematic Hybrid Search and Confidence Scorer integration, combined with a dual In-Domain/Out-of-Context evaluation framework, provides a practical and empirically grounded configuration guideline for building hallucination-resistant RAG systems in low-resource local-culture domains.
Co-Authors Alif Abdul Hakim Arib Arkaan Fadhlullah Chalifa Chazar Devit Saputra Dhiki Romadinur Firhan Hafiansyah Marisa Premitasari, Marisa Milda Gustiana Husada Milda Gutiana Husada Muhammad Daffa Fathur Rahman Nirmala Putri Ismail NUR FITRIANTI F Petrick Juliansz Kakalang Rangga Alfyansyah RIDHO FARUQI Rizka Milandga Milenio Tegar Subagdja