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Syahroni Hidayat
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INDONESIA
Jurnal Teknologi Informasi dan Multimedia
Published by Puslitbang Sekawan Institute Nusa Tenggara
ISSN : 27152529     EISSN : 26849151     DOI : https://doi.org/10.35746/jtim.v2i1
Core Subject : Science,
Computer Science & IT
Cakupan dan ruang lingkup JTIM terdiri dari Databases System, Data Mining/Web Mining, Datawarehouse, Artificial Integelence, Business Integelence, Cloud & Grid Computing, Decision Support System, Human Computer & Interaction, Mobile Computing & Application, E-System, Machine Learning, Deep Learning, Information Retrievel (IR), Computer Network & Security, Multimedia System, Sistem Informasi, Sistem Informasi Geografis (GIS), Sistem Informasi Akuntansi, Database Security, Network Security, Fuzzy Logic, Expert System, Image Processing, Computer Graphic, Computer Vision, Semantic Web, Animation dan lainnya yang serumpun dengan Teknologi Informasi dan Multimedia.
Arjuna Subject : -
Articles 275 Documents
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Wavelet-Based MFCC and CNN Framework for Automatic Detection of Cleft Speech Disorders Muhammad Hilmy Herdiansyah; Syahroni Hidayat; Nur Iksan
Jurnal Teknologi Informasi dan Multimedia Vol. 7 No. 3 (2025): August
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v7i3.780

Abstract

Cleft Lip and Palate (CLP) is a congenital condition that often results in atypical speech articulation, making automatic recognition of CLP speech a challenging task. This study proposes a deep learning-based classification system using Convolutional Neural Networks (CNN) and Wavelet-MFCC features to distinguish speech patterns produced by CLP individuals. Specifically, we investigate the use of two wavelet families Reverse Biorthogonal (rbio1.1) and Biorthogonal (bior1.1)—with three decomposition strategies: single-level (L1), two-level (L2), and a combined level (L1+2). Speech data were collected from 10 CLP patients, each pronouncing nine selected Indonesian words ten times, resulting in 900 utterances. The audio signals were processed using wavelet-based decomposition followed by Mel-Frequency Cepstral Coefficients (MFCC) extraction to generate time-frequency representations of speech. The resulting features were input into a CNN model and evaluated using 5-fold cross-validation. Experimental results show that the combined L1+2 decomposition yields the highest classification accuracy (92.73%), sensitivity (92.97%), and specificity (99.04%). Additionally, certain words such as “selam”, “kapak”, “baju”, “muka”, and “abu” consistently achieved recall scores above 0.94, while “lampu” and “lembab” proved more difficult to classify. The findings demonstrate that integrating multi-level wavelet decomposition with CNN significantly improves the recognition of pathological speech and offers promising potential for clinical diagnostic support.
Rancang Bangun Sistem Satu Data sebagai Strategi Peningkatan Mutu Pendidikan Tinggi di Universitas Mataram Mohammad Zaenuddin Hamidi; Ahmad Zafrullah Mardiansyah; I Wayan Sudiarta; Halil Akhyar
Jurnal Teknologi Informasi dan Multimedia Vol. 7 No. 3 (2025): August
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v7i3.781

Abstract

The quality of higher education greatly depends on the accuracy and consistency of data used in decision making and accreditation processes. At the University of Mataram, various information systems have been developed separately, such as the Academic Information System (SIA), the New Student Admission System (PMB), the Research and Community Service Information System (SIMLITABMAS), and the Lecturer Performance Information System (BKD). However, the lack of integration among these systems has resulted in fragmented data, which is often unsynchronized and complicated the quality reporting process. This study aims to design and develop an integrated information system based on the One Data approach, which consolidates various primary data sources into a centralized platform within the University of Mataram. The advantages of the developed system lie in its direct integration with PDDIKTI, enabling automated data retrieval and submission through the national central system. This research adopts a Research and Development (R&D) approach with a Waterfall based software development method, consisting of the stages of requirement analysis, system design, implementation, testing, and evaluation. The results show that the developed system is able to integrate data from various existing systems and presenting it in a consistent format that is easily accessible by relevant units, with testing results achieving a 100% success rate. The system has been proven to support efficiency in reporting and monitoring processes for higher education quality in a more accurate manner. In conclusion, the One Data system contributes significantly to improve the quality of data governance and quality assurance processes in higher education and can serve as a foundation for the development of a more holistic and sustainable academic data ecosystem.
Optimizing Inter-Site Traffic Comparative Performance Analy-sis of IPSec with IKEv2 RSA-ESP and IKEv2 with PSK Surya Pratama; Mohammad Ramaddan Julianti; Detin Sofia
Jurnal Teknologi Informasi dan Multimedia Vol. 7 No. 3 (2025): August
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v7i3.788

Abstract

This study compares the performance of IPsec VPNs using Internet Key Exchange version 2 (IKEv2) with RSA and Pre-Shared Key (PSK) authentication. The research is driven by the rising need for secure and efficient communication in distributed systems, particularly in environments with limited resources and sensitivity to latency. Guided by the PPDIOO framework, this study assesses system performance across two distinct scenarios: standard operational conditions and impaired (stressed) network environments. Key metrics include latency, jitter, throughput, packet loss, and IKE negotiation time, measured using iperf3, ping, and tc netem. The testbed uses virtual Ubuntu environments with strongSwan 5.9.13 on VMware® Workstation, simulating inter-site traffic VPNs. Under normal conditions, PSK outperforms RSA by showing lower latency (0.82 ms vs. 0.88 ms), faster IKE setup (10.05 ms vs. 20.80 ms), and higher UDP throughput. Under stressed conditions—100 ms latency, 20 ms jitter, and 1% packet loss—PSK remains more resilient, especially for real-time UDP traffic. RSA offers steady performance for TCP downloads. Statistical significance is confirmed using paired t-tests. The results suggest PSK suits lightweight deployments with minimal cryptographic demands, while RSA is better for environments requiring certificate-based security. This study provides valuable insights for network architects in selecting appropriate IPsec configurations based on specific operational requirements. Future research may explore scalability considerations, multi-user environments, and the integration with Software-Defined Wide Area Networking (SD-WAN) technologies.
Analisis Prediksi Penjualan Isi Ulang Air Galon menggunakan Metode LSTM dan SARIMA Ulfarida Miftakhul Jannah; Nurmalitasari Nurmalitasari; Ridwan Dwi Irawan
Jurnal Teknologi Informasi dan Multimedia Vol. 7 No. 3 (2025): August
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v7i3.802

Abstract

Refillable drinking water depots often face challenges in dealing with unpredictable customer demand on a daily basis. This uncertainty complicates the process of stock management, production planning, and overall operations. Without accurate sales forecasts, depots risk losing potential sales and experiencing a decline in service quality to customers. Therefore, a solution is needed that can accurately predict daily sales. The first step in this research is to collect relevant data. Once the data is available, pre-processing is conducted to prepare the data before entering the modeling process. The Long Short-Term Memory (LSTM) model has the advantage of remembering historical patterns in time series data. Meanwhile, the Seasonal Autoregressive Integrated Moving Average (SARIMA) model is an extension of ARIMA that can handle data with seasonal characteristics. In this study, the LSTM model demonstrated better performance than SARIMA. This is evidenced by the performance evaluation values: MAPE of 9.54%, RMSE of 0.17, and MAE of 0.14 for the LSTM model, which are lower than MAPE of 10.51%, RMSE of 0.19, and MAE of 0.16 for SARIMA. These values indicate that LSTM is capable of providing more accurate prediction results. Based on these results, it can be concluded that the LSTM model is more effective and recommended for use in predicting daily sales of refillable water at the Manshurin Water depot
Implementasi Augmented Reality Sebagai Media Pembelajaran Untuk Pengenalan Buah-Buahan Rahman Nur Syam; Sigit Sugiyanto; Tito Pinandita; Feri Wibowo
Jurnal Teknologi Informasi dan Multimedia Vol. 7 No. 3 (2025): August
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v7i3.805

Abstract

Innovative and interactive teaching strategies have emerged as a result of the development of communication and information technology. One of the most promising and rapidly expanding educational technologies is augmented reality. By displaying virtual things in three dimensions in a real-world setting in real time, Augmented Reality can make studying more engaging and joyful for students Augmented reality can display virtual objects in three dimensions in real time, creating a more engaging and enjoyable learning experience for students. This research aims to develop and implement Augmented Reality-based fruit recognition learning media as an alternative to conventional, static and unengaging media for elementary school students, helping them visualize the concepts being learned. The Multimedia Development Life Cycle (MDLC), which has six stages concept, design, gathering materials, assembly, testing, and distribution the development methodology. This application is designed to display various types of fruit as 3D objects that can be scanned through markers using the camera on an android device. Each fruit is equipped with its own name and information to improve student knowledge. Testing is carried out through black box testing to evaluate system functions, and user feasibility testing using a Likert scale questionnaire given to 15 grade students. According to the results of black-box testing, there were no system or functional issues and the application operated as planned. It received an 84.86% feasibility score, placing it in the "very feasible" range. Thus, it can be said that this AR-based fruit recognition app works well to boost students' curiosity, involvement, and comprehension of the material.

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