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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) KARMAPATI (Kumpulan Artikel Mahasiswa Pendidikan Teknik Informatika) ISSN: 2252-9063 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 Jurnal Teknologi Informasi dan Ilmu Komputer 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 Dinasti International Journal of Education Management and Social Science 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 Penelitian Sistem Informasi Jurnal INFOTEL Journal of Digital Literacy and Volunteering Journal of Digital Literacy and Volunteering Journal of Digital Market and Digital Currency
M Said Hasibuan
Institut 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 Tingkat Kematangan Keamanan Informasi Menggunakan Indeks KAMI pada Tiyuh Pulung Kencana R, Khristina Henny; Hasibuan, M. 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.v2i1.78

Abstract

Everyone must adapt quickly if they don't want to be left behind due to very rapid and significant technological developments. All institutions in Indonesia must also apply information technology to expedite and make their activities more effective and fulfill their duties and functions in implementing work programs. For this reason, the role of information security is very important to maintain the confidentiality, integrity and availability of information for the public and service interests. Based on the conditions described above, it is important to carry out a SID (Village Information System) assessment to determine the maturity and completeness of data security. Tiyuh Pulung Kencana Village in Tulang Bawang Tengah District, West Tulang Bawang Regency is the object of this research. This village or Tiyuh is one of the villages that has implemented SID effectively in the implementation of the Lampung Province Smart Village Program. The Information Security Index (KAMI) is used in Tiyuh Pulung Kencana, Tulang Bawang Tengah District, West Tulang Bawang Regency, to assess the completeness and maturity of the information security system. Document analysis and interviews with related parties are the qualitative methods used. The results of the KAMI index research show that Tiyuh Pulung Kencana relies heavily on electronic systems, but steps to protect information are still just starting (only in the initial stages). Therefore, Tiyuh Pulung Kencana must improve its information security practices by creating policies and procedures that comply with IT/IS standards and resource readiness.
Penerapan Gaya Belajar Kolb, Honey And Mumford, Felder Silverman, Vark: Study Empiris pada Mahasiswa Pendidikan Teknologi Informasi IIB Darmajaya Febriana, Annisa Arsya; Sari, Novita; Kumala, Dian Agustin Arta; Selfiyana, Reva; Hasibuan, Muhammad Said
Journal of Digital Literacy and Volunteering Vol. 3 No. 1 (2025): January
Publisher : Puslitbang Akademi Relawan TIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57119/litdig.v3i1.134

Abstract

Learning styles play a significant role in shaping the learning process of each individual. This study employs a descriptive qualitative approach and involves 15 students from the Information Technology Education Study Program, consisting of 7 male and 8 female participants. Data were collected using four learning style questionnaires: VARK, Kolb’s Learning Style Inventory, Honey and Mumford’s Learning Styles Questionnaire, and the Felder-Silverman Learning Style Model. The objective of this research is to broaden instructional strategies by incorporating learning styles that align with students’ preferences. The findings underscore the importance of utilizing teaching methods that are congruent with students’ learning preferences to enhance academic achievement.
Karakteristik Gaya Belajar: (Studi Empiris Pada Mahasiswa Pendidikan Teknologi Informasi Iib Darmajaya) Khodijah, Siti; Situmorang, Klaudia SB; Nuryana, Sapta Adi; Hasibuan, Muhammad Said
Journal of Digital Literacy and Volunteering Vol. 3 No. 1 (2025): January
Publisher : Puslitbang Akademi Relawan TIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57119/litdig.v3i1.135

Abstract

This study aims to describe the characteristics of students towards aspects of 4 different learning styles, Perceptions of VARK, Felder Silverman, Kolbs and Honey and Mumford learning styles. The population in this study were students of the Information Technology Education Study Program at the Darmajaya Institute of Informatics and Business with a total of 25 students, This study had 15 participants with a percentage of Men (46.6%) and Women (53.4%), in addition there were 10 students who did not participate in this study. Data were collected through questionnaires which were then analyzed using descriptive statistics. The results of the study showed differences in each individual who has a variety of learning styles, for that understanding a learning style can make it easier for students to implement learning strategies that are of interest according to their wishes so that Information Technology Education students can increase creativity and effectiveness to improve learning achievement.
PELATIHAN PEMBUATAN MEDIA PEMBELAJARAN INTERAKTIF BERBASIS CLASSPOINT BAGI GURU di PROPINSI LAMPUNG Arman Suryadi Karim; Melda Agarina; Sutedi; M. Said Hasibuan; M. Royan Fauzi
J-ABDI: Jurnal Pengabdian kepada Masyarakat Vol. 2 No. 3: Agustus 2022
Publisher : Bajang Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53625/jabdi.v2i3.2986

Abstract

Kemajuan yang terjadi dalam dunia teknologi komunikasi dan informasi memunculkan peluang maupun tantangan baru dalam dunia pendidikan. Peluang baru yang muncul termasuk akses yang lebih luas terhadap konten multimedia yang lebih kaya, dan berkembangnya metode pembelajaran baru yang tidak lagi dibatasi oleh ruang dan waktu. Dalam pembelajaran tersebut, muncul masalah baru system pembelajaran yang terjadi selama ini, masih banyak pembelajaran daring yang terkesan dipaksakan karena kondisi pandemi. Sehingga hal tersebut dapat menyebabkan banyak keluhan, baik dari kalangan peserta didik, orang tua, bahkan dari pihak guru sendiri. Masalah tersebut dapat disebabkan oleh guru yang tidak terampil dalam mendayagunakan teknologi sebagai media belajar kreatif. Sehingga pembelajaran akan membuat peserta didik jenuh. Maka tim Pengabdian berfokus dalam memberikan pelatihan pembuatan media pembelajaran interaktif agar pembelajaran menjadi lebih menyenangkan dengan menggunakan aplikasi ClassPoint. Metode yang diterapkan dalam kegiatan pelatihan ini adalah Metode Presentasi mengenai pengenalan software, kemanfaataannya, dan penerapannya dalam pembuatan media pembelajaran interaktif, Metode Demonstrasi mengenai pengoperasionalisasian program dan Metode Praktik yaitu pembuatan media pembelajaran secara langsung oleh peserta sesuai dengan mata pelajaran masing-masing dengan pemanfaatan aplikasi Class Point. Kegiatan pelatihan tersebut berjalan lancar dan kini para tenaga pendidik dapat membuat media pembelajaran menjadi lebih interaktif dengan pemanfaatan aplikasi tersebut
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.
Co-Authors A Adven Tonny A Feriyanto Abdi Darmawan Achmad Aldi Sakoni Adam Japal Adi Wijaya Admi Syarif Afdal Wahyu Prayuda Agus, Isnandar Aldi Mawaridi Aldi Mawaridi Ali Nasution Andry Ferianto anggalia wibasuri Anton Setiawan Anuar Sanusi Anuar Sanusi Arbi Gunawan ARDIANSYAH ARDIANSYAH Ari Rohmawati Arie Setya Putra Arman Suryadi Karim, Arman Suryadi Astri Agustiani Aziz, RZ. Abdul Bagus Yuda Pratama Baruna Wisnu Wardana Baskoro Baskoro Dani Apriansyah Danil, Sapni Della Niken Indriani Delli Maria Denny Prastiawan Destiawan Destiawan Dewi, Deshinta Arrova Dika Tondo W Diki Andita Kusuma Doni Andrianto Dworo, Dworo Dzaki Al Hafiz Eko Budi Wicaksono Eko Zulkaryanto Elis Malana Faurani Santi Singagerda Fauziah Zahra Ramadhani Febriana, Annisa Arsya Fely Dany Prasetya Fernando, Rhino Firdaus Chaniago Firmansyah Firmansyah Firmansyah Firmansyah Firmansyah Firmansyah Firmansyah Firmansyah Tri Wahyudi Fitria Fransiska, Devi Gumelar, Muhamad Agung Guntur Tiara Wahyu Hidayah Hafina Oktaviani handoyo widi nugroho Hardiansyah, Deni Hariyanto Wibowo Henderi . Hendri Purnomo Heri Nurdiyanto Herwanto, Riko Ismail, Abdul Azim Bin Isnaini Bastari Iwan Tri Bowo Kaesar Azra Putra Zeva Khristina Henny R Komang Wardiasa Kumala, Dian Agustin Arta Kurnia Muludi Kurniawan Kurniawan, Tri Basuki Laila, Siti Nur M. Arif Prayoga M. Arif Rifai M. Natsir Hendy Tri Putra M. Nugrahadi Rakhman M. Royan Fauzi Mahfut Maizary, Ary Marzuki Marzuki Melda Agarina Melda Agharina Muhammad Fahmi Hafidz Muhammad Haris Hermanto Mukhas Munif Ahsani Munaa Munaa Munaa, Munaa Mutiara, Suci Nathan Nurdadyansyah Nathan, Yogeswaran Netty Sefriyanti Nosiel Nosiel Novi Herawadi S Novi Herawadi Sudibyo Novi Herawadi Sudibyo, Novi Herawadi Novita Sari Nurfiana Nurfiana Nurhayati Nurhayati 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 Rahmalia Syahputri Rahmalia Syahputri Rahmalia Syahputri Ramadhani, Fauziah Zahra Rangga Firdaus Ratih Pratiwi Ratna Nurhaya Renita Dwi Astuti Ridho Kurniawan RIDHO KURNIAWAN, RIDHO Rio Gismara Rizky Yulizar Rahman Romadhoni, Nuzul Rahmat Rosandi, Triowali Ruki Rizal Sakoni, Achmad Aldi Sapni Danil Savitri, Ratna Selfiyana, Reva Setiyono . Sigit Andriyanto Singh, Harprith Kaur Rajinder Siswahyudianto Siti Khodijah Situmorang, Klaudia SB Sri Ratna Sulistiyanti Sutedi Sutedi Suwandi Tetra Praja Utama Tri Winarti Triloka, Joko Triowali Rosandy Wahyu Bintono Wasilah Wibaselppa, Anggawidia Widi Nugroho, Handoyo Winda Rika Lestari Y Verawati Y. Suhendro Yeh, Ming-Lang Yeni Purnamasari Yogi Maulana Yoni Hisbullah Yudha, Efrian Prama Yusuf, Suhendro Zainal A. Hasibuan Zakaria, Mohd Zaki