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All Journal bit-Tech International Journal of Data Science, Engineering, and Analytics (IJDASEA)
Mohammad Idhom
UPN "Veteran" Jawa Timur
Computer Science & IT Decision Sciences, Operations Research & Management

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Soil Movement Monitoring System Based on IoT using Fuzzy Logic Mohammad Idhom; Fetty Tri Anggraeny; Gideon Setya Budiwitjaksono; Zainal Abidin Achmad; Munoto
Internasional Journal of Data Science, Engineering, and Anaylitics Vol. 1 No. 2 (2021): International Journal of Data Science, Engineering, and Analytics Vol 1, No 2,
Publisher : International Journal of Data Science, Engineering, and Analytics

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (833.846 KB) | DOI: 10.33005/ijdasea.v1i2.14

Abstract

Landslide is one of the disasters that often occurs in several areas in Indonesia, especially in hilly areas, valleys, and volcanoes. Soil conditions in some parts of Indonesia are classified as prone to landslides. The latest data from the Central Statistics Agency related to landslides in 2018 occurred as many as 10,246 events with the highest incidence on the island of Java IoT-based ground motion monitoring using fuzzy logic is a tool that is able to detect ground movements that can trigger landslides. The manufacture of this tool is based on the ig-norance of the community in predicting the occurrence of landslides. To avoid this, an early warning tool is needed in the delivery of information that is easily understood by anyone, especially the public. This tool consists of a Microcontroller, Weather Sensor, Rain Sensor, Ground Movement Sensor, and GSM Shield as well as programs to make it hap-pen. This system was created to provide information to the public directly in land-slide-prone areas. With this early warning system, it is hoped that people who are in landslide-prone loca-tions will know more quickly and can monitor the condition of landslide-prone areas so that they will be more alert to possible dangers that come suddenly, especially fatalities, can be minimized. Through this tool can also be known when the weather is cloudy, raining as well as movement or signs of ground movement, can be monitored and monitored automatically. directly by everyone from mobile phones through "SIPEGERTA" Land Movement System in Wonosalam District, Jombang Regency
Integrated System for Evaluation of Implementation of Internal Quality Audits and ISO 9001; 2015 Case Study : Universitas Pembangunan Nasional “Veteran” Jawa Timur Mohammad Idhom; Jojok Dwiridotjahjono; I Gede Susrama Mas Diyasa; Rheza Rizqi Ahmadi; Munoto
Internasional Journal of Data Science, Engineering, and Anaylitics Vol. 1 No. 2 (2021): International Journal of Data Science, Engineering, and Analytics Vol 1, No 2,
Publisher : International Journal of Data Science, Engineering, and Analytics

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1426.565 KB) | DOI: 10.33005/ijdasea.v1i2.15

Abstract

The Internal Quality Assurance System (SPMI) is a system to ensure quality in the process of providing education. All components in the process of providing education support the achievement of aspects of SPMI. An important role in SPMI is the scope of the study program (Prodi), faculties, Institute for Learning Development and Quality Assurance (LP3M), and reviewers. Study program/faculty as SPMI document compiler. LP3M acts as system manager and decision maker at SPMI. Reviewers as assessors who assess the results of the SPMI study program documents. In SPMI activities, study programs / faculties fill out the required form files. Then the reviewer can evaluate the completed file with a score from 1 to 4. However, the evaluation process which is also called Internal Quality Audit (AMI) is still manual. This makes it less easy for LP3M managers to monitor evaluation values ​​and make decisions. From the description above, this proposal proposes a system that can perform an integrated evaluation of AMI online. Not only focusing on AMI, SITEPAMIS can also conduct evaluations to meet ISO 9000;2015. ISO 9000;2015 is a standard for quality management. This research is divided into two years. In the first year, the creation of a web technology-based system with evaluation features of AMI and ISO 9000;2015 values ​​until the implementation process. The output of this stage is a SITEPAMIS web application, reputable national journals, national seminars, and copyrights. In the second year, the mobile version of SITEPAMIS was started. The output of this stage is a SITEPAMIS mobile application, international journals, international seminars, and textbooks, so that at the end of the research results in an Integrated System Application for Evaluation of Internal Quality Audit Implementation and an ISO version of SITEPAMIS which is purely Web-based.
Hybrid Prediction Model Fuzzy Time Series-LSTM on Stock Price Data with Volatility Variation Alfi Hidayatur; Mohammad Idhom; Wahyu Syaifullah
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.v8i2.3014

Abstract

Predicting stock prices in volatile markets remains a major challenge in financial analysis because irregular fluctuations often undermine the reliability of conventional models. Traditional methods such as ARIMA struggle to capture nonlinear dynamics and the complex dependencies that characterize financial time series. To address this gap, this study proposes a hybrid forecasting model that integrates Fuzzy Time Series (FTS) with Long Short-Term Memory (LSTM). The FTS component helps manage uncertainty and simplifies volatility patterns, while the LSTM network captures sequential dependencies across time. Together, these elements provide a more adaptive representation of stock price behavior under different volatility levels. The model was applied to datasets representing both high and low volatility in the Indonesian stock market. Performance was assessed using Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). Results show that the hybrid model achieved high accuracy in low-volatility data with an MAE of 284.36 and a MAPE of 0.039%. In high-volatility conditions it also maintained robust performance with an MAE of 885.85 and a MAPE of 0.53%. These outcomes indicate that combining fuzzy logic with deep learning offers a promising approach for stock prediction under volatility variation. The integration not only enhances the reliability of forecasting but also provides a basis for future exploration of risk-aware applications in financial analysis.
A Deep Learning Approach Using Bidirectional-LSTM and Word2Vec for Fake News Classification Fadhilah Nur Hidayat; Wahyu Syaifullah J. S; Mohammad Idhom
bit-Tech Vol. 8 No. 3 (2026): bit-Tech - IN PROGRESS
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3575

Abstract

The rapid growth of online news consumption in Indonesia has intensified the challenge of combating fake news, which undermines public trust and threatens social stability. Conventional approaches, including manual verification, are increasingly inadequate to address the scale and speed of digital information dissemination. This study aims to develop an automatic Indonesian fake news classification system using a deep learning framework that integrates Bidirectional Long Short-Term Memory (Bi-LSTM) with Word2Vec embeddings. Unlike many existing fake news detection models that rely on limited validation settings or focus predominantly on English-language data, this work explicitly addresses the linguistic characteristics and practical constraints of the Indonesian context, thereby strengthening model relevance for real-world deployment. The dataset comprises 6,000 balanced news articles, including 3,000 valid items from Detik.com and 3,000 hoax items from Turnbackhoax.id, collected between January and October 2024. Text preprocessing involved cleaning, stopword removal, tokenization, and padding. A 300-dimensional Word2Vec embedding model was employed, and the classifier was trained using stratified 3-fold cross-validation to ensure robust performance estimation. An ensemble inference strategy was further applied to reduce inter-fold variance and enhance generalization on unseen data, directly addressing a common limitation of prior single-model approaches. Experimental results show that the proposed model achieves an accuracy of 86.43% and an F1-score of 86.28%, alongside a high mean Average Precision of 0.927 during validation. Compared with previously reported deep learning baselines, this framework demonstrates competitive yet more stable performance under realistic evaluation settings, supporting scalable deployment.
Co-Authors Alfi Hidayatur Fadhilah Nur Hidayat Fetty Tri Anggraeny Gideon Setya Budiwitjaksono I Gede Susrama Mas Diyasa Jojok Dwiridotjahjono Munoto Rheza Rizqi Ahmadi Wahyu Syaifullah Wahyu Syaifullah J. S Zainal Abidin Achmad