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All Journal INTI Nusa Mandiri Journal of Information Systems and Technology Research Jurnal Sains dan Teknologi
Arifan Rahman
Universitas Andalas
Agriculture, Biological Sciences & Forestry Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Physics Transportation

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Digitalization of Rural Water Management: Android-Based Billing for Community Systems using the ADDIE model Nurfiah Nurfiah; Afdhal Dinilhak; Luthfil Khairi; Budy Satria; Anggi Hadi Wijaya; Ajeng Dwi Asti; Arifan Rahman
Journal of Information Systems and Technology Research Vol. 4 No. 2 (2025): May 2025
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v4i02.1135

Abstract

The integration of information technology in everyday life has changed the way people work, learn, socialize, make transactions, and make decisions. The use of Android-based smartphones is a real example of the use of technology. Android, which is open-source, has encouraged the development of applications widely according to need. Access to water is a fundamental human right. PAMSIMAS is a flagship program of the regional and central governments that seeks to meet water needs through the provision of clean water services in line with the Sustainable Development Goals (SDGs). In Durian Seribu Village, PAMSIMAS is a service to meet the water needs of the community and become a solution for rural communities to get clean water at low cost, but its management is still manual, such as recording water usage and billing, so it is inefficient, time-consuming, and prone to errors. From these problems, this study proposes the development and implementation of an Android application designed to simplify the recording and billing process for the PAMSIMAS program in Durian Seribu Village. This application aims to simplify management, increase data transparency, and simplify reporting. The results of tests that have been carried out using the black box method show that this application can facilitate officers in recording and billing payments for PAMSIMAS water usage. Officers only need to enter the total water usage, and the application will automatically calculate and print a receipt as proof of payment. Officers also do not need to calculate manually when reporting the total payment to the administrator. For administrators, this application makes it easier to monitor and evaluate the performance of recording officers. After the application was used for recording and billing, PAMSIMAS's revenue increased by around 30% from the revenue before using the application.
Klasifikasi Emosi Suara Berbasis Citra Spektogram Menggunakan Jaringan Saraf Konvolusional Muhammad Dawi Syauqi; Arifan Rahman; Ajeng Dwi Asti; Nurfiah Nurfiah
Jurnal Sains Dan Teknologi | E-ISSN : 3063-9980 Vol. 2 No. 4 (2026): April - Juni
Publisher : GLOBAL SCIENTS PUBLISHER

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Abstract

Speech Emotion Recognition (SER) is a crucial area in human-computer interaction, where selecting effective feature representations remains a key challenge. This study proposes an SER system using Log-Mel Spectrogram images and a 2D Convolutional Neural Network (CNN). The Toronto Emotional Speech Set (TESS) dataset, comprising 2,800 audio samples across 7 balanced emotion classes (400 samples each), was employed. Each audio file was converted into a 128×128-pixel Log-Mel Spectrogram image. The CNN architecture consists of three convolutional blocks (32, 64, and 128 filters) with Batch Normalization and Max Pooling, followed by a Fully Connected layer (128 neurons), Dropout (rate 0.5), and a 7-class Softmax output layer. The model was trained using the Adam optimizer, with early stopping triggered at epoch 22. Experimental results demonstrate that the model achieves 100% classification accuracy on 560 test samples, yielding perfect precision, recall, and F1-score (1.00) for all classes. This research confirms that visual spectrogram representations combined with CNNs can automatically and effectively extract emotional features without manual feature engineering.
Analisis Ketahanan Fitur MFCC dan Log-Mel Spektrogram untuk Speech Emotion Recognition Berbasis CNN pada Berbagai Kondisi Signal-to-Noise Ratio Rifki Yuliandra; Arifan Rahman; Afdhal Dinilhak; Luthfil Khairi; Anggi Hadi Wijaya
Jurnal Sains Dan Teknologi | E-ISSN : 3063-9980 Vol. 2 No. 4 (2026): April - Juni
Publisher : GLOBAL SCIENTS PUBLISHER

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Abstract

Speech Emotion Recognition (SER) has achieved remarkable performance under controlled, clean-audio conditions; however, its robustness in noise-laden, real-world environments remains insufficiently characterized. This study investigates the performance degradation of a Convolutional Neural Network (CNN)-based SER system on the RAVDESS dataset when subjected to synthetic noise at various Signal-to-Noise Ratio (SNR) levels (−5, 0, 5, 10, and 15 dB). We compare two widely used feature representations: Mel-Frequency Cepstral Coefficients (MFCC) and Log-Mel Spectrogram. Both models were trained exclusively on clean audio and evaluated under twelve noise conditions using Additive White Gaussian Noise (AWGN) and babble noise. Experimental results on the RAVDESS dataset (2,880 samples, 8 emotion classes) reveal a distinct asymmetry: the MFCC-based CNN achieves a 48.84% clean-audio accuracy with a maximum degradation of 27.08 percentage points (pp). Conversely, the Log-Mel Spectrogram model achieves a higher clean baseline of 66.67% but suffers a severe drop of up to 47.69 pp under noise, approaching the random baseline of 12.5%. These findings demonstrate that MFCC features offer superior robustness to additive noise due to implicit spectral smoothing via mel filterbanks and Discrete Cosine Transform (DCT), despite exhibiting lower clean-audio discriminability. This research highlights a fundamental trade-off between feature discriminability and noise robustness in uncontrolled acoustic environments.
DETEKSI DAN KLASIFIKASI PENYAKIT DAUN TANAMAN PORANG MENGGUNAKAN MODEL YOLOV8 Muhammad Al Hafiz; Budy Satria; Arifan Rahman
INTI Nusa Mandiri Vol. 21 No. 1 (2026): INTI Periode Agustus 2026
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v21i1.8748

Abstract

Porang (Amorphophallus muelleri) is a high-value agricultural commodity whose productivity can be significantly reduced by leaf diseases. Conventional visual inspection is time-consuming and subjective, highlighting the need for an automated detection system that is both accurate and efficient. Previous studies on porang leaf diseases have primarily focused on image classification or object detection methods with limited capability to generalize under varying visual conditions. Therefore, this study aims to evaluate the performance of the YOLOv8n model integrated with data augmentation for detecting and classifying five conditions of porang leaves: early blight, late blight, konjac mosaic, insect attacks, and healthy leaves. The proposed approach combines YOLOv8n with on-the-fly data augmentation during training and Stratified 5-Fold Cross-Validation to improve the model's generalization capability. A total of 1,065 leaf images were used for model training and evaluation. Data augmentation techniques, including Mosaic, HSV color adjustment, and geometric transformations, were applied during training. Experimental results show that the proposed model achieved a mean Average Precision (mAP50) of 80.53%, a Precision of 83.11%, and a Recall of 70.66%, outperforming the baseline model without augmentation, which obtained an mAP50 of only 63.15%. Although the early blight and healthy classes achieved excellent detection performance (mAP50 > 90%), the relatively lower Recall was mainly caused by background bias in the insect class, resulting in several objects being missed during detection. Overall, the integration of YOLOv8n with data augmentation demonstrates its potential to improve the generalization capability of porang leaf disease detection systems under diverse visual conditions.
Co-Authors Afdhal Dinilhak Ajeng Dwi Asti Anggi Hadi Wijaya Budy Satria Luthfil Khairi Muhammad Al Hafiz Muhammad Dawi Syauqi Nurfiah Rifki Yuliandra