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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Teknika PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnal Ilmu Komputer dan Agri-Informatika Jurnal Informatika Proceeding International Conference on Information Technology and Business Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) International conference on Information Technology and Business (ICITB) Jurnal SIMADA (Sistem Informasi dan Manajemen Basis Data) Proceeding of the Electrical Engineering Computer Science and Informatics International Journal of Artificial Intelligence Research INTEGER: Journal of Information Technology Faktor Exacta JMM (Jurnal Masyarakat Mandiri) Jurnal Teknoinfo JUTIM (Jurnal Teknik Informatika Musirawas) Jurnal Informasi dan Komputer Jurnal Teknik Elektro dan Komputasi (ELKOM) Jurnal Infortech Jurnal Generic Journal of Applied Data Sciences Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Bioscientist : Jurnal Ilmiah Biologi Jurnal Pengabdian kepada Masyarakat Journal of Research in Social Science and Humanities International Journal of Advanced Science and Computer Applications TECHSI - Jurnal Teknik Informatika Jurnal INFOTEL Journal of Digital Literacy and Volunteering Journal of Digital Literacy and Volunteering Journal of Digital Market and Digital Currency
M Said Hasibuan
Institute Informatika Dan Bisnis Darmajaya
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Public Health Social Sciences Other

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Analisis Keamanan Informasi pada Sistem Komputerisasi Terpadu Menggunakan Metode Indeks KAMI dan Octave Allegro Hasibuan, Muhammad Said; Romadhoni, Nuzul Rahmat; Muludi, Kurnia
Jurnal Ilmu Komputer dan Agri-Informatika Vol. 12 No. 1 (2025)
Publisher : Sekolah Sains Data, Matematika, dan Informatika. Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jika.12.1.38-49

Abstract

Transformasi digital meningkatkan pelayanan publik melalui teknologi, seperti Sistem Komputerisasi Terpadu di Kementerian XYZ, yang memproses data secara terpadu. Namun, ancaman keamanan data menjadi perhatian utama. Upaya mitigasi melibatkan Indeks KAMI untuk evaluasi keamanan berbasis ISO 27001 dan metode OCTAVE Allegro untuk identifikasi risiko aset informasi, sehingga mendukung pengelolaan data yang aman dan andal. Penelitian ini dimulai dengan identifikasi masalah keamanan informasi, dilanjutkan tinjauan pustaka terkait teori, standar, dan metode seperti Indeks KAMI dan Octave Allegro. Data dikumpulkan melalui observasi, wawancara, dan kuesioner, lalu dianalisis menggunakan kedua metode tersebut. Berdasarkan penilaian Indeks KAMI menunjukkan skor 570 dengan predikat “Cukup Baik”. Sedangkan dalam penilaian Octave Allegro menghasilkan 4 dari 5 area risiko memiliki kategori mitigate or transfer dan 1 area lainya berkategori defer. Risiko seperti pencurian perangkat dapat ditangani kantor kabupaten, sementara risiko besar seperti peretasan atau kegagalan backup ditransfer ke kantor pusat untuk mitigasi lebih lanjut. Analisis keamanan informasi dengan Indeks KAMI dan Octave Allegro menunjukkan bahwa kantor kabupaten memiliki pencapaian baik dalam kepatuhan ISO 27001, namun pengelolaan risiko masih bergantung pada kantor pusat. Octave Allegro lebih efektif dalam mengidentifikasi dan menangani risiko, sehingga cocok digunakan untuk instansi dengan kewenangan yang terbatas.
Integrating Convolutional Neural Networks into Mobile Health: A Study on Lung Disease Detection Hasibuan, Muhammad Said; Isnanto, R Rizal; Dewi, Deshinta Arrova; Triloka, Joko; Aziz, RZ Abdul; Kurniawan, Tri Basuki; Maizary, Ary; Wibaselppa, Anggawidia
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.660

Abstract

This study presents the development and evaluation of a Convolutional Neural Network (CNN) model for lung disease detection from chest X-ray images, complemented by a mobile application for real-time diagnosis. The CNN model was trained on a diverse dataset comprising images labeled as "NORMAL" and "PNEUMONIA," achieving an overall accuracy of 96%. Compared to traditional machine learning methods such as Support Vector Machine (SVM) and Random Forest, which typically achieve accuracies ranging from 85% to 92%, the proposed CNN model demonstrates superior performance in classifying lung conditions. The model achieved high precision (0.98) and recall (0.96) for pneumonia detection, as well as precision (0.89) and recall (0.95) for normal cases, ensuring both sensitivity and specificity in diagnostic performance. These results indicate that the model minimizes false positives and false negatives, which is crucial for reducing misdiagnoses and improving patient outcomes in clinical settings. To enhance accessibility, an Android-based application was developed, allowing users to upload chest X-ray images and receive instant diagnostic results. The application successfully integrated the trained CNN model, offering a user-friendly interface suitable for healthcare professionals and patients alike. User testing demonstrated reliable performance, facilitating timely and accurate lung disease detection, particularly in areas with limited access to radiologists. These findings highlight the potential of CNNs in medical imaging and the critical role of mobile technology in expanding healthcare accessibility. This innovative approach not only improves diagnostic accuracy but also enables real-time disease detection, ultimately supporting clinical decision-making. Future research will focus on expanding the dataset, incorporating additional lung conditions, and optimizing the model for enhanced robustness in diverse clinical scenarios.
Incorporate Transformer-Based Models for Anomaly Detection Dewi, Deshinta Arrova; Singh, Harprith Kaur Rajinder; Periasamy, Jeyarani; Kurniawan, Tri Basuki; Henderi, Henderi; Hasibuan, M. Said; Nathan, Yogeswaran
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.762

Abstract

This paper explores the effectiveness of Transformer-based models, specifically the Time-Series Transformer (TST) and Temporal Fusion Transformer (TFT), for anomaly detection in streaming data. We review related work on anomaly detection models, highlighting traditional methods' limitations in speed, accuracy, and scalability. While LSTM Autoencoders are known for their ability to capture temporal patterns, they suffer from high memory consumption and slower inference times. Though efficient in terms of memory usage, the Matrix Profile provides lower performance in detecting anomalies. To address these challenges, we propose using Transformer-based models, which leverage the self-attention mechanism to capture long-range dependencies in data, process sequences in parallel, and achieve superior performance in both accuracy and efficiency. Our experiments show that TFT outperforms the other models with an F1-score of 0.92 and a Precision-Recall AUC of 0.71, demonstrating significant improvements in anomaly detection. The TST model also shows competitive performance with an F1-score of 0.88 and Precision-Recall AUC of 0.68, offering a more efficient alternative to LSTMs. The results underscore that Transformer models, particularly TST and TFT, provide a robust solution for anomaly detection in real-time applications, offering improved performance, faster inference times, and lower memory usage than traditional models. In conclusion, Transformer-based models stand out as the most effective and scalable solution for large-scale, real-time anomaly detection in streaming time-series data, paving the way for their broader application across various industries. Future work will further focus on optimizing these models and exploring hybrid approaches to enhance detection capabilities and real-time performance.
Detecting Gender-Based Violence Discourse Using Deep Learning: A CNN-LSTM Hybrid Model Approach Kurniawan, Tri Basuki; Dewi, Deshinta Arrova; Henderi, Henderi; Hasibuan, M. Said; Zakaria, Mohd Zaki; Ismail, Abdul Azim Bin
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.761

Abstract

Gender-Based Violence (GBV) is a critical social issue impacting millions worldwide. Social media discussions offer valuable insights into public awareness, sentiment, and advocacy, yet manually analyzing such vast textual data is highly challenging. Traditional text classification methods often struggle with contextual understanding and multi-class categorization, making it difficult to accurately identify discussions on Sexual Violence, Physical Violence, and other topics. To address this, the present study proposes a hybrid deep learning approach combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. CNN is utilized for extracting key linguistic features, while LSTM enhances the classification process by maintaining sequential dependencies. This hybrid CNN+LSTM model is evaluated against standalone CNN and LSTM models to assess its performance in classifying GBV-related tweets. The dataset was sourced from Kaggle, containing real-world Twitter discussions on GBV. Experimental results demonstrate that the hybrid model surpasses both CNN and LSTM models, achieving an accuracy of 89.6%, precision of 88.4%, recall of 89.1%, and F1-score of 88.7%. Confusion matrix and ROC curve analyses further confirm the hybrid model’s superior performance, correctly identifying Sexual Violence (82%), Physical Violence (15%), and Other (3%) cases with reduced misclassification rates. These results suggest that combining CNN’s feature extraction with LSTM’s contextual learning provides a more balanced and effective classification model for GBV-related text. This work supports the development of AI-based tools for social media monitoring, policy-making, and advocacy, helping stakeholders better understand and respond to GBV discussions. Future research could explore transformer-based models like BERT and real-time classification applications to further improve performance.
The Classification Method is Used for Sentiment Analysis in My Telkomsel Hardiansyah, Deni; Aziz, RZ Abdul; Hasibuan, Muhammad Said
International Journal of Artificial Intelligence Research Vol 8, No 2 (2024): December 2024
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v8i2.1229

Abstract

User reviews significantly impact how mobile apps are perceived and provide developers with valuable insights into improving the functionality and quality of their products. Sentiment analysis of these evaluations helps identify the main issues faced by consumers, such as technical difficulties, costs, and service levels. The main objective of this study is to classify user sentiment into positive and negative categories, focusing on the MyTelkomsel app. With the use of Google Play Scraper, 39,493 reviews on various app versions and user experiences were collected. This data was analyzed using multiple machine learning models, including Support Vector Machines (SVM), Naive Bayes, Random Forest, and Gradient Boosting, alongside the Natural Language Processing (NLP) approach. The results show that 39.2% of the reviews are positive, while 60.8% reflect negative sentiment. Among the models, SVM showed the highest accuracy in sentiment classification with a value of (0.854792), while Naive Bayes (0.775541), Random Forest (0.829725), and Gradient Boosting (0.819344) also performed well in sentiment classification. These findings suggest that developers can leverage the insights gained from this analysis to proactively improve the performance and user experience of the MyTelkomsel app, by addressing technical and service-related issues identified in user reviews.
An Artificial Neural Network-Based Geo-Spatial Model for Real-Time Flood Risk Prediction Using Multi-Source High-Resolution Data Aziz, RZ Abdul; Nurpambudi, Ramadhan; Herwanto, Riko; Hasibuan, Muhammad Said
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.913

Abstract

Flood prediction presents a pressing challenge in disaster management, especially in regions vulnerable to extreme weather events. In response, this study offers a novel approach to flood risk prediction by developing a deep learning-based Geo-Spatial Artificial Neural Network (ANN). The model actively integrates high-resolution satellite imagery, meteorological data, and topographic indicators, such as rainfall, elevation, and land use to capture complex spatial and environmental relationships that influence flood risk. This study conducted data preprocessing using Principal Component Analysis (PCA) and normalization to ensure consistency across datasets. It built the ANN with multiple hidden layers and trained it using the backpropagation algorithm on historical flood data. Furthermore, it designed the ANN model with multiple hidden layers and trained it using the backpropagation algorithm. The model achieved a notable 92% prediction accuracy, significantly outperforming traditional flood prediction methods, which typically yield 75–85% accuracy. Conventional metrics were Mean Squared Error (1.41) and R-squared (0.94). It confirmed the model’s superior ability to predict high-risk flood zones. The model also effectively captured non-linear patterns that conventional statistical or deterministic methods often failed to detect. The results showed that the model generalizes well and adapts effectively, making it suitable for real-time and data-driven flood forecasting. By integrating artificial intelligence with geo-spatial analytics, this study offers a scalable, accurate, and efficient tool for early warning systems and risk management. It recommends that future research should focus on incorporating additional data sources and refining model training techniques to further enhance scalability and performance.
Evaluation of Information Security at the Radin Inten II Lampung Meteorological Station Using the KAMI Index Ardiansyah, Ardiansyah; Irianto, Suhendro Yusuf; Hasibuan, M. Said
Bioscientist : Jurnal Ilmiah Biologi Vol. 12 No. 2 (2024): December
Publisher : Department of Biology Education, FSTT, Mandalika University of Education, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33394/bioscientist.v12i2.12498

Abstract

Information security is a way to protect information assets from various potential threats. BMKG is a Non-Departmental Government Institution (LPND) in Indonesia whose main duties involve carrying out government duties in the fields of meteorology, climatology and geophysics. In connection with delivering information services appropriately and precisely to stakeholders, the Radin Inten II Lampung Meteorological Station needs to carry out an independent assessment in terms of security to evaluate the information system in each work unit, with the aim of understanding the level of readiness and maturity of information security. This research aims to measure the level of information security maturity at the Radin Inten II Lampung Meteorological Station. The analysis method used in this research is using the KAMI Index version 5.0 based on the ISO/IEC 27001:2022 standard. The research results indicate that the implementation of the ISO 27001:2022 standard in the information system of the Radin Inten II Lampung Meteorological Station is considered good. The total score obtained reached 591 based on analysis and questionnaires using the KAMI Index. With this score, the Radin Inten II Lampung Meteorological Station information system is categorized at level III, which indicates that some improvements are still needed.
Implementasi Teknologi QR Code Dalam Pendaftaran Calon Mahasiswa Baru Prayuda, Afdal Wahyu; Sakoni, Achmad Aldi; Prayoga, M. Arif; Hasibuan, Muhammad Said
Journal of Digital Literacy and Volunteering Vol. 1 No. 1 (2023): January
Publisher : Puslitbang Akademi Relawan TIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57119/litdig.v1i1.14

Abstract

In tertiary institutions, the acceptance of new students (PMB) is an administrative procedure. Online registration forms are filled out by prospective students to complete the registration procedure. As a result, a system was created that makes use of QR Code technology, which is anticipated to make the registration process easier for potential new students (PMB). The system design approach that was utilized to create the design for the software requirements analysis system was the technique employed in this paper. In order to find out the processes that happen in a system based on the results of the Qr Code Design as a Service Optimization Media for New Student Admission Registration, researchers carry out software requirements in the design and construct optimization of new student admissions services.
Pengembangan Sistem Absensi Karyawan di Institut Informatika dan Bisnis Darmajaya Dengan Menggunakan Teknologi Barcode Pratama, Bagus Yuda; Ramadhani, Fauziah Zahra; Munaa, Munaa; Hasibuan, Muhammad Said
Journal of Digital Literacy and Volunteering Vol. 1 No. 1 (2023): January
Publisher : Puslitbang Akademi Relawan TIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57119/litdig.v1i1.15

Abstract

There are so many ways to process employee attendance, one of which is to use the manual method. So far, there are still many large companies that still implement attendance manually, but this causes a lot of time leaks or other violations, which of course makes it less effective and efficient and causes attendance information to be inaccurate. In a company with quite a lot of employees, it is very necessary to have proper, fast, accurate attendance management. An accurate methodology for solving problems in this modern era with the use of QR barcodes because it will really help companies to attend to employees in real time. The system is made with the PHP programming language and uses the MYSQL database. The purpose of QR Code (Quick Response Code) technology in companies is as a tool in processing employee attendance data, employee identity cards and also processing employee data which is beneficial for employees because they can carry out computerized attendance activities. The results to be obtained from the research and implementation of this system are by entering several examples of employee data as an experimental form of attendance transactions, and the attendance application program is made to run properly. The system created produces several features in the form of user features, checking QR codes for attendance, generating QR codes from each employee card, recapitulation and attendance reports on the system, and employee data in the form of employee names, positions, work shifts and work location placements
Evaluasi Sistem ATR/BPN Berbasis Webuse dan Heuristic Evaluation Danil, Sapni; Hasibuan, Muhammad Said
Journal of Digital Literacy and Volunteering Vol. 2 No. 1 (2024): January
Publisher : Puslitbang Akademi Relawan TIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57119/litdig.v2i1.41

Abstract

The Ministry of Agrarian Spatial Planning/National Land Agency (ATR/BPN) provides services that aim to facilitate the public in managing the legality of land services online, which can be accessed anywhere with the aim of providing convenience and security to the public in conducting service transactions as an effort to increase the ease of doing business, but the benefits of this website have not been measured for the usability of the website effectively and efficiently. Therefore, this research was conducted with the aim of evaluating and assessing the appearance of the interface with the Heuristic Evaluation method and the Web Usability Evaluation Tool (WEBUSE) and measuring display development. ATR/BPN website to help the community. The research method uses the Web Usability Evaluation Tool (WEBUSE) in the form of a questionnaire and then evaluates it using Heuristic Evaluation. The research that has been carried out has produced findings that show the ATR/BPN Website has a level that is good enough to be used by Notary/PPAT employees and the community. From the usability test, it shows that the quality attribute can be said to be of high value. The conclusion of the study shows that each category of WEBUSE Evaluation has a usability value of 0.69 points, which indicates the usability level is at a good level while the lowest value is in the visibility of system status (feedback) variable (H1 of 61.02%) according to the usability level at the Moderate. This means it's not working properly. And the Savery Rating is 1.3119 when rounded up to 1 category of cosmetic problem that takes time to fix.
Co-Authors - Nurfiana A Adven Tonny A Feriyanto Abdi Darmawan Abror , Muhamad Achmad Aldi Sakoni Adam Japal Adi Wijaya Admi Syarif Afdal Wahyu Prayuda Agus, Isnandar Ali Nasution Andry Ferianto anggalia wibasuri Anuar Sanusi Anuar Sanusi Arbi Gunawan ARDIANSYAH ARDIANSYAH Ari Rohmawati Arie Setya Putra Arman Suryadi Karim, Arman Suryadi Aziz, RZ. Abdul Bagus Yuda Pratama Baruna Wisnu Wardana Baskoro Baskoro Dani Apriansyah Danil, Sapni Delli Maria Denny Prastiawan Destiawan Destiawan Dewi, Deshinta Arrova Dian Saputra Dika Tondo W Diki Andita Kusuma Doni Andrianto Dworo, Dworo Effendi, M. Junius Eko Budi Wicaksono Eko Zulkaryanto Elis Malana Fauziah Zahra Ramadhani Febriana, Annisa Arsya Fely Dany Prasetya Fernando, Rhino Firmansyah Firmansyah Firmansyah Firmansyah Firmansyah Fitria Fransiska, Devi Guntur Tiara Wahyu Hidayah handoyo widi nugroho Hardiansyah, Deni Henderi . Hendri Purnomo Herawadi S, Novi Hermansyah, Idi Herwanto, Riko Ismail, Abdul Azim Bin Isnaini Bastari Iwan Tri Bowo Khristina Henny R Kumala, Dian Agustin Arta Kurnia Muludi Kurniawan Kurniawan, Tri Basuki Laila, Siti Nur M. Arif Prayoga M. Arif Rifai M. Royan Fauzi Mahfut Maizary, Ary Marzuki Marzuki Melda Agarina Melda Agharina Muhammad Fahmi Hafidz Mukhas Munif Ahsani Munaa Munaa Munaa, Munaa Nathan Nurdadyansyah Nathan, Yogeswaran Netty Sefriyanti Nosiel Nosiel Novi Herawadi Sudibyo Novi Herawadi Sudibyo, Novi Herawadi Novita Sari Nurdiyanto, Heri Nurpambudi, Ramadhan Nuryana, Sapta Adi Onno W Purbo Periasamy, Jeyarani Pratama, Bagus Yuda Pratama, Tomy Adi Prayoga, M. Arif Prayuda, Afdal Wahyu Prilian Ayu Winarni Purbo, Onno W R Rizal Isnanto R, Khristina Henny Rahmadi, Lendy Rahmalia Syahputri Rahmalia Syahputri Ramadhani, Fauziah Zahra Rangga Firdaus Ratih Pratiwi Ratna Nurhaya Renita Dwi Astuti Ridho Kurniawan RIDHO KURNIAWAN, RIDHO Rizky Yulizar Rahman Romadhoni, Nuzul Rahmat Rosandi, Triowali Ruki Rizal Sakoni, Achmad Aldi Sapni Danil Savitri, Ratna Selfiyana, Reva Setiyono . Sigit Andriyanto Singagerda, Faurani Santi Singh, Harprith Kaur Rajinder Siti Khodijah Situmorang, Klaudia SB SRI RAHAYU Sri Ratna Sulistiyanti Suci Mutiara Sutedi Sutedi Suwandi Tetra Praja Utama Triloka, Joko Wahyu Bintono Wasilah Wibaselppa, Anggawidia Winda Rika Lestari Y Verawati Y. Suhendro Yeh, Ming-Lang Yogi Maulana Yoni Hisbullah Yudha, Efrian Prama Yusuf, Suhendro Zainal A. Hasibuan Zakaria, Mohd Zaki