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All Journal Jiko (Jurnal Informatika dan komputer) Progresif: Jurnal Ilmiah Komputer Jurnal Teknologi Informasi dan Terapan (J-TIT) bit-Tech Djtechno: Jurnal Teknologi Informasi Seminar Nasional Teknologi dan Multidisiplin Ilmu
Mohammad Idhom
Universitas Pembangunan Nasional "Veteran" Jawa Timur
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Social Sciences

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Analisis Website Pusat Pengembangan Karir Berdasarkan Pengalaman Pengguna: Pendekatan User Experience Questionnaire (UEQ) Aris Pratama; Siti Mukaromah; Mohammad Idhom; Asif Faroqi; Tri Lathif Mardi Suryanto; Rizky Parlika
Jurnal Teknologi Informasi dan Terapan Vol 10 No 2 (2023)
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v10i2.321

Abstract

Pembangunan National “Veteran” University of East Java has developed the Career Development Center website as a job vacancy information center to prepare graduates who are tough, innovative, and creative in the world of work. The Career Development Center website makes it easier to search for and find job opportunities that suit the field of study, skills, and interests of college graduates. A positive user experience in using the Career Development Center website can increase satisfaction and optimize career searches, so it is important to carry out measurements systematically and objectively. This study aims to measure the level of experience of users of the Career Development Center website using the User Experience Questionnaire (UEQ). UEQ measurements on the Career Development Center website focus on gaining an understanding of the Attractiveness, Pragmatic quality, and Hedonic quality aspects. UEQ provides measurements of technical and non-technical aspects related to the user's emotion or perception of pleasure. The results of measuring user experience on the Career Development Center website can help identify trends and changes in user needs so that they can adapt and develop relevant and innovative features. This study uses quantitative data by distributing 26 items of the UEQ questionnaire online to 350 alumni. The research results stated that the attractiveness aspect had the highest average value of 1.94. These results prove that the Career Development Center website has a high attractiveness value for users, thereby increasing the opportunity for continuous use. Apart from that, based on the high average stimulation value of 1.930, it can be proven that graduates who use the Career Development Center website get large benefits related to alumni career development.
Application of Multivariate Singular Spectrum Analysis for Weather Prediction Abdul Mukti; Kartika Maulida Hindrayani; Mohammad Idhom
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3003

Abstract

Weather significantly influences various aspects of life, especially in urban areas like Surabaya, where unpredictable weather can disrupt transportation, public health, economic activities, and overall comfort. Among the key meteorological variables, air temperature and relative humidity are crucial for assessing human thermal comfort, as their interaction forms the heat index a key indicator of health risks in tropical regions. This study introduces the use of the Multivariate Singular Spectrum Analysis (MSSA) method to forecast daily weather parameters, including minimum temperature (TN), maximum temperature (TX), average temperature (TAVG), and average relative humidity (RH_AVG). The research utilized weather data from the Perak 1 Meteorological Station in Surabaya, spanning from August 1 to December 31, 2024 (training data) and January 1 to January 14, 2025 (testing data). Unlike traditional methods, the MSSA model effectively analyzes the complex relationships between multiple weather variables, improving forecasting accuracy. The model demonstrated strong performance, with Mean Absolute Percentage Errors (MAPE) of 3.70% for TN, 5.99% for TX, 4.44% for TAVG, and 7.39% for RH_AVG. These results highlight MSSA's potential as an effective tool for short-term weather forecasting in urban tropical environments, supporting more accurate predictions that can inform early warning systems, disaster planning, and public health strategies. This work advances the state-of-the-art by offering a robust method for handling multivariate weather data, which is essential for making informed decisions in rapidly changing climates
Effectiveness of Extreme Learning Machine in Online Payment Transaction Fraud Detection Radya Ardi; Mohammad Idhom; Kartika Maulida Hindrayani
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3005

Abstract

The rise of fintech and digital payment systems has increased efficiency but also escalated the risk of online transaction fraud, particularly under imbalanced data conditions where fraudulent cases are rare. This study addresses the limitations of traditional rule-based and machine learning models in such scenarios by proposing the use of Extreme Learning Machine (ELM) with hyperparameter tuning as a novel and efficient solution for fraud detection. Unlike most prior studies relying on default settings or data resampling, this research focuses on enhancing ELM performance purely through parameter optimization using the Optuna framework. A dataset of 20,000 real-world online transactions was used to evaluate model performance before and after tuning. In its default configuration, ELM yielded high overall accuracy (96.80%) but failed to detect fraudulent cases (0% recall and F1-score). After tuning key parameters such as the number of hidden neurons and activation function, the model achieved a significantly better balance between accuracy and fraud detection performance, with 99.53% accuracy, 98.20% precision, 86.51% recall, and a 91.98% F1-score. These results demonstrate that hyperparameter tuning alone, without resampling, can substantially improve ELM’s sensitivity to minority class detection. The findings suggest that optimized ELM offers a promising alternative for real-time fraud detection in imbalanced financial datasets, contributing to more adaptive and reliable security systems in the digital finance landscape.
Indonesian Sign Language (SIBI) Recognition from Audio Mel-Spectrograms Using LSTM Architecture Enryco Hidayat; Mohammad Idhom; Afina Lina Nurlaili
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3229

Abstract

Persistent communication barriers continue to challenge Deaf and Hard of Hearing (DHH) individuals in accessing spoken language, underscoring the need for effective and inclusive translation technologies. Existing audio-to-sign language systems typically employ multi-stage pipelines involving speech-to-text transcription, which may propagate recognition errors and fail to preserve acoustic nuances. Addressing these limitations, this study developed and evaluated a deep learning framework for translating spoken Indonesian audio directly into classifications of the Indonesian Sign Language System (SIBI), eliminating explicit text conversion. The dataset comprised 495 eight-second WAV recordings (22,050 Hz) representing five SIBI phrase classes, augmented through time stretching, pitch shifting, and noise addition to improve generalization. Mel-Spectrogram features were extracted and input to a stacked Long Short-Term Memory (LSTM) network implemented in TensorFlow/Keras, trained to learn temporal–spectral mappings between audio patterns and SIBI categories. Evaluation on a held-out test set demonstrated robust performance, achieving 98 % accuracy with consistently high precision, recall, and F1-scores. The trained model was further integrated into a prototype web application built with Flask and React, confirming its feasibility for real-time assistive communication. While results highlight the viability of direct Mel-Spectrogram-to-LSTM translation for SIBI recognition, current findings are constrained by the limited dataset size and restricted speaker diversity. Future research should therefore expand the dataset to include more speakers, varied acoustic environments, and continuous-speech inputs to ensure broader applicability and real-world robustness.
FORECASTING SALES USING SARIMA MODELS AT THE SINAR PAGI BUILDING MATERIALS STORE Ahmad Adiib Aminullah; Mohammad Idhom; Wahyu Syaifullah Jauharis Saputra
JIKO (Jurnal Informatika dan Komputer) Vol 7 No 2 (2024)
Publisher : Program Studi Teknik Informatika Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i2.8266

Abstract

Sinar Pagi Building Materials Store faces the challenge of maintaining optimal stock levels of goods to avoid excess and understock, which affects customer satisfaction and operational efficiency. This study applies the Seasonal Autoregressive Integrated Moving Average (SARIMA) method to forecast sales in the store. Leveraging its ability to model seasonal patterns on historical sales data, various SARIMA models were analyzed and compared using the Akaike Information Criterion (AIC) and Root Mean Square Error (RMSE). The dataset is divided by a 95:5 ratio into training and testing sets for robust evaluation. The results show that the SARIMA model with SARIMA notation (p,d,q)(P,D,Q  has the best model value of (1,0,0) . This model is the most suitable model based on the lowest AIC value of 1245 and the lowest RMSE of 7,95 compared to other SARIMA models after model identification using the model looping test. For other models such as model (1,0,1)  and (0,0,1) , the AIC and RMSE values are greater, namely model (1,0,1)  with AIC 1246 and RMSE of 8,05, while model (0,0,1)  gets an AIC of 1252 and an AIC of 8,15 .The lower the AIC value, the better the model and the lower the RMSE value, the better the model. This shows a superior balance between model complexity and prediction accuracy. The model manages to capture seasonal patterns in sales data, providing a pretty good prediction framework. This study shows that the SARIMA (1,0,0)  model is effective in the accuracy of the sales forecasting process so that Sinar Pagi Building Materials Store can make more reliable sales predictions, which can help in inventory planning and marketing strategies
Deteksi Serangan DDoS pada Trafik IoT Menggunakan Random Forest dengan Dataset CICIoT2023 Muchammad Basroil Billah; Mohammad Idhom; Hendra Maulana
Progresif: Jurnal Ilmiah Komputer Vol 22, No 2 (2026): April
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v22i2.3614

Abstract

As the number of Internet of Things (IoT) devices continues to grow, these devices become increasingly vulnerable to Distributed Denial of Service (DDoS) attacks. However, their limited computational capacity makes it difficult to implement conventional security mechanisms. This study proposes a model for detecting DDoS attacks using Random Forest, trained using the CICIoT2023 dataset, which consists of 46 flow-based features collected from 105 real-world IoT devices. The preprocessing stage includes binary classification, normalization using StandardScaler, and handling class imbalance through a combination of 1:10 undersampling and class weighting. Evaluation on 1,154,684 test samples shows excellent performance, achieving 99.99% accuracy, 100% precision, 99.99% recall, and 99.99% F1-score. To ensure reliability, six validation checks are conducted, including overfitting analysis, cross-validation. The results confirm that the model can generalize well beyond the training data. Most attack types are detected perfectly, although application-layer attacks such as DDoS-SlowLoris remain more challenging. Overall, Random Forest proves to be an effective and relatively lightweight approach for DDoS detection in IoT environments.Keywords: DDoS; Random Forest; IoT; CICIoT2023; Machine LearningAbstrakPertumbuhan jumlah perangkat IoT menyebabkan peningkatan risiko terhadap berbagai ancaman keamanan terhadap serangan Distributed Denial of Service (DDoS). Namun, keterbatasan kapasitas komputasi pada perangkat IoT menyulitkan penerapan mekanisme keamanan konvensional. Penelitian ini mengusulkan model deteksi DDoS berbasis Random Forest yang dilatih menggunakan dataset CICIoT2023, yang terdiri dari 46 fitur berbasis flow yang dikumpulkan dari 105 perangkat IoT nyata. Tahap preprocessing meliputi klasifikasi biner, normalisasi menggunakan StandardScaler, serta penanganan ketidakseimbangan kelas melalui kombinasi undersampling (1:10) dan class weighting. Hasil evaluasi pada 1.154.684 data uji menunjukkan performa yang sangat tinggi, dengan accuracy sebesar 99,99%, precision 100%, recall 99,99%, dan F1-score 99,99%. Untuk memastikan keandalan model, dilakukan enam pengujian validasi, termasuk analisis overfitting, cross-validation. Hasil penelitian mengonfirmasi bahwa model mampu melakukan generalisasi dengan baik terhadap data di luar data pelatihan. Sebagian besar jenis serangan berhasil dideteksi secara sempurna, meskipun serangan pada lapisan aplikasi seperti DDoS-SlowLoris masih menjadi tantangan. Secara keseluruhan, Random Forest terbukti sebagai pendekatan yang efektif dan relatif ringan untuk deteksi DDoS pada lingkungan IoT Kata kunci: DDoS; Random Forest; IoT; CICIoT2023; Machine Learning
Prediksi Harga Saham Menggunakan ARIMA Outlier sebagai Pendekatan Awal Menuju Analisis AI Keuangan Cindi Adam; Mohammad Idhom; Trimono Trimono
Seminar Nasional Teknologi dan Multidisiplin Ilmu (SEMNASTEKMU) Vol. 5 No. 1 (2025): SEMNASTEKMU
Publisher : Universitas Sains dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/zwvk1v20

Abstract

The development of artificial intelligence (AI) has driven innovation in financial analysis, including the prediction of volatile stock prices. This study aims to predict the stock price of PT Garudafood Putra Putri Jaya Tbk using an ARIMA model with Outlier handling as an initial approach towards a more adaptive prediction system. Daily closing price data from Yahoo Finance was analyzed through stationarity testing, ARIMA model identification, log-return-based Outlier detection, and performance evaluation using RMSE, MAE, and MAPE. The results show that ARIMA Outlier performs better than the basic ARIMA. The standard ARIMA produces a MAPE of 1.32% and an AIC of –899.46, while ARIMA with three dummy Outliers achieves a MAPE of 1.16% and an AIC of –900.37. The 14-day forecast shows a stable pattern in the range of Rp 370–371. In the test data, the basic ARIMA provided the best accuracy in mid-August, while ARIMA Outlier achieved the highest accuracy at the end of August with a prediction of Rp 370.2, which was very close to the actual price of Rp 370.4. These results show that handling Outliers improves the accuracy of the model, so that ARIMA Outlier can be used as a starting point for the development of an AI-based financial prediction system.
PERBANDINGAN ALGORITMA HDBSCAN DAN AGGLOMERATIVE HIERARCHICAL CLUSTERING DALAM MENGELOMPOKKAN DATA KETENAGAKERJAAN YANG OUTLIERS Citra Amelia Intan Permadani; Aviolla Terza Damaliana; Mohammad Idhom
Djtechno: Jurnal Teknologi Informasi Vol 6, No 2 (2025): Agustus
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/djtechno.v6i2.7237

Abstract

Ketenagakerjaan merupakan indikator penting dalam mendukung pembangunan ekonomi nasional. Namun, distribusi tenaga kerja di Indonesia masih menunjukkan ketimpangan antarprovinsi. Beberapa provinsi memiliki kontribusi ekonomi dan tingkat pekerjaan formal yang tinggi, sementara yang lain tertinggal. Penelitian ini bertujuan mengidentifikasi pola distribusi ketenagakerjaan antarprovinsi dengan menerapkan analisis klaster menggunakan delapan variabel dari data BPS. Mengingat adanya pencilan dalam data, deteksi outlier dilakukan menggunakan metode Local Outlier Factor (LOF) yang mengidentifikasi enam provinsi sebagai outlier yaitu Jawa Barat, Jawa Tengah, Jawa Timur, DKI Jakarta, Banten, dan Sumatera Utara. Selanjutnya, data dianalisis menggunakan dua pendekatan klasterisasi, yaitu Agglomerative Hierarchical Clustering (Single, Complete, Average Linkage, dan Ward) dan HDBSCAN untuk membandingkan ketahanan metode terhadap data outlier. Validasi kualitas klaster dilakukan dengan Silhouette Coefficient. Hasil menunjukkan bahwa metode Single Linkage memiliki nilai koefisien tertinggi, namun kurang konsisten dalam memisahkan outlier. Sebaliknya, HDBSCAN lebih adaptif terhadap data yang mengandung noise dan pencilan dengan Silhouette Coefficient sebesar 0.546. Dengan demikian, HDBSCAN dinilai lebih efektif dalam analisis klasterisasi data ketenagakerjaan yang kompleks, sementara metode AHC lebih unggul dalam membentuk klaster yang jelas jika pencilan dapat ditangani secara terpisah.
Design and Development of an IoT-Based Rain Intensity Prediction System Using LoRa M. Arif; Mohammad Idhom; Henni Endah Wahanani
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3704

Abstract

An Internet of Things (IoT)–based system for rain intensity monitoring and next-day prediction is presented by integrating low-power wide-area communication using LoRa with cloud-based processing for outdoor and rural environments. This study evaluates the feasibility of LoRa communication and the end-to-end operational reliability of an IoT–cloud pipeline, while positioning machine learning as a supporting decision-aid module. A low-cost sensing node equipped with temperature, humidity, and wind-speed sensors is connected to a LoRa-based gateway that forwards measurements to an Amazon EC2 cloud server via MQTT for centralized storage, processing, and notification delivery. The system is evaluated through a 10-day single-node real-world outdoor deployment, focusing on sensor data acquisition reliability, LoRa link quality, and end-to-end operation from data acquisition to user notifications. The classification module achieves an overall accuracy of 0.74 with a weighted F1-score of 0.71, while minority-class performance remains limited due to class imbalance. LoRa communication remains stable with RSSI values of −80.91 to −79.19 dBm, SNR values of 9.86–9.95 dB, and packet loss rates below 3%. By jointly evaluating LPWAN communication reliability and cloud-side predictive services within a single field deployment, the results demonstrate the practicality of LPWAN-based IoT sensing with cloud integration for rain intensity monitoring in resource-constrained environments, while highlighting the need for future improvements in minority-class prediction and multi-node scalability.
Co-Authors Abdul Mukti Afina Lina Nurlaili Ahmad Adiib Aminullah Aris Pratama Asif Faroqi Aviolla Terza Damaliana Cindi Adam Citra Amelia Intan Permadani Enryco Hidayat Henni Endah Wahanani Kartika Maulida Hindrayani M. Arif Maulana, Hendra Muchammad Basroil Billah Parlika, Rizky Radya Ardi Trimono Trimono Wahyu Syaifullah Jauharis Saputra