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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jatilima : Jurnal Multimedia Dan Teknologi Informasi Instal : Jurnal Komputer
Sailellah, Hassan Rizky Putra
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Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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Analysis and Implementation of an EMC-Based Radio Frequency Emission Measurement System Ariyanto, Endro; Yudo, Yogi Anggun Saloko; Sailellah, Hassan Rizky Putra
Bahasa Indonesia Vol 17 No 10 (2025): Instal : Jurnal Komputer
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jurnalinstall.v17i10.433

Abstract

The rapid advancement of electronic technology brings many benefits, but it also introduces risks of interference in the form of electromagnetic wave emissions that may affect the performance of nearby electronic devices. To ensure that devices maintain proper electromagnetic compatibility (EMC), precise and standardized methods for measuring radio-frequency emissions are required. This study focuses on developing radio-frequency emission measurement techniques and creating software capable of controlling a spectrum analyzer, displaying measurement results, storing data, and performing further analysis. The methods used follow the RTCA/DO-160C standard, covering both conducted and radiated emission measurements through voltage conversion and the application of device correction factors. Testing was carried out using a functional (black-box) approach on each main feature of the SpectrumAnalyzer object. The results demonstrate that all software functions operate correctly, from device initialization, data retrieval and storage, to spectrum normalization. Overall, the development and testing results confirm that the RFESW software is capable of performing radio-frequency emission measurements effectively and complies with the requirements of the RTCA/DO-160C standard.
Investigating Shallow Learning Methods for Optical Character Recognition of Indonesia’s Nusantara Scripts Sulistiyo, Mahmud Dwi; Putrada, Aji Gautama; Ihsan, Aditya Firman; Yunanto, Prasti Eko; Richasdy, Donny; Sailellah, Hassan Rizky Putra; Sabrina Adinda Sari
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.6648

Abstract

Indonesia has numerous regional scripts—or so-called Nusantara scripts—and recognizing them is important to preserve Indonesia's cultural heritage. The advances of AI and computer vision technologies make it possible for a machine to optically read the handwritten scripts through the Optical Character Recognition (OCR) technique. However, collecting some of the top OCR solutions and comprehensively investigating their performances on the Nusantara scripts is currently lacking. This study investigates and evaluates some shallow learning-based methods on our newly introduced datasets, consisting of more than 38,000-character images across 80 letter classes in total; here, we focus on three regional scripts: Javanese, Sundanese, and Balinese. The methods include Random Forest, SVM, Logistic Regression, and Gaussian Naïve Bayes, as well as boosting techniques such as XGBoost, Light GBM, and CatBoost. A 5-fold cross-validation approach assessed model performance based on accuracy, precision, recall, and F1-score. Based on the experimental results, the methods demonstrated their competitiveness in reaching the best models for scripts; in particular, XGBoost, Light GBM, and Random Forest-Gini were the winners for Javanese, Sundanese, and Balinese scripts, respectively. These findings demonstrate the effectiveness of ensemble learning methods for diverse handwritten scripts. Comparative analysis to prior deep learning studies is also discussed in this paper. In addition, this research also contributes to preserving Indonesian traditional scripts, as well as offers insights for future regional OCR in other countries.
Round-Trip Time Estimation Using Hybrid Neuro-Fuzzy Based On Subtractive Clustering Sailellah, Hassan Rizky Putra; Ristianto, Suma Danu
Jurnal Multimedia dan Teknologi Informasi (Jatilima) Vol. 7 No. 06 (2026): Jatilima : Jurnal Multimedia Dan Teknologi Informasi
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jatilima.v7i06.2159

Abstract

Round-trip time (RTT) is a key latency indicator for quality-of-service (QoS) control and task orchestration in cloud–edge systems. However, RTT is highly time-varying due to congestion dynamics, routing changes, and fluctuating traffic conditions, motivating short-term prediction to enable proactive decision making. This paper investigates a hybrid neuro-fuzzy baseline for RTT prediction implemented using an Adaptive Neuro-Fuzzy Inference System (ANFIS) with subtractive-clustering-based initialization to avoid rule explosion in high-dimensional inputs. A controlled dataset was generated in Mininet using a dumbbell topology with injected delays (1–1000 ms). In total, 100,000 raw RTT records were collected (100 RTT measurements per run across 1000 runs) and aggregated into 1000 supervised samples paired with TCP-state features. Experiments followed a unified and reproducible protocol with a fixed 60/10/30 train/validation/test split, train-only feature standardization, train-only target normalization with inverse transformation for reporting, and validation-based checkpoint selection. The ANFIS baseline (radius ????=0.5r=0.5, 19 rules) achieved RMSE/ MAE/ MAPE/ ????2 of 1191.63/ 751.02/ 0.001921/ 0.999996 on validation and 1207.23/ 664.70/ 0.001311/ 0.999996 on testing. Training required 546.91 s, while inference remained lightweight (0.0846 s for 100 validation samples and 0.1493 s for 300 test samples). Diagnostic analyses using learning curves, parity plots, residual inspection, and empirical error distributions further supported the strong agreement between predicted and observed RTT values. These results indicate that ANFIS with subtractive clustering can deliver accurate and low-latency RTT prediction suitable for QoS-aware orchestration pipelines where training can be performed offline.
Co-Authors Aditya Firman Ihsan Aji Gautama Putrada Endro Ariyanto Mahmud Dwi Sulistiyo Prasti Eko Yunanto Richasdy, Donny Ristianto, Suma Danu Sabrina Adinda Sari Yudo, Yogi Anggun Saloko