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All Journal JSI: Jurnal Sistem Informasi (E-Journal) Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Penelitian Pendidikan IPA (JPPIPA) Cyberspace: Jurnal Pendidikan Teknologi Informasi Journal of Electrical Engineering and Computer (JEECOM) Brilliance: Research of Artificial Intelligence Heca Journal of Applied Sciences Transcendent Journal of Mathematics and Applications J-SIGN (Journal Of Informatics, Information System, And Artificial Intelligence) Infolitika Journal of Data Science PESARE: Science and Engineering Service Journal The Indonesian Journal of Computer Science
Misbullah, Alim
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Evaluating Geothermal Power Plant Sites with Additive Ratio Assessment: Case Study of Mount Seulawah Agam, Indonesia Azhar, Fauzul; Misbullah, Alim; Lala, Andi; Idroes, Ghazi Mauer; Kusumo, Fitranto; Noviandy, Teuku Rizky; Irvanizam, Irvanizam; Idroes, Rinaldi
Heca Journal of Applied Sciences Vol. 2 No. 1 (2024): March 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/hjas.v2i1.158

Abstract

Indonesia, a country rich in geothermal resources, has yet to fully exploit its potential, particularly in volcanic regions like Mount Seulawah Agam. This study investigates the application of the Additive Ratio Assessment (ARAS) method for the site selection of Geothermal Power Plants (GPP) in Indonesia. The ARAS method provides a systematic approach to evaluating and prioritizing geothermal development sites by integrating multiple criteria, including geological, environmental, and socio-economic factors. The study collects data from various sources and weights criteria using the Ordinal Priority Approach (OPA), incorporating expert opinions. The findings demonstrate the effectiveness of the ARAS method in identifying optimal locations for GPP development, ensuring sustainability and feasibility. The study also tests the ARAS method in existing GPP locations in Jaboi, Sabang, Indonesia, to investigate alignment with the results and validate the approach. Furthermore, the study presents recommendations for GPP site selection. This research emphasizes the significance of multi-criteria decision-making techniques in facilitating renewable energy projects. It promotes a more systematic and informed approach to geothermal energy development in Indonesia and other geothermal-rich regions.
Optimizing Geothermal Power Plant Locations in Indonesia: A Multi-Objective Optimization on The Basis of Ratio Analysis Approach Rahman, Isra Farliadi; Misbullah, Alim; Irvanizam, Irvanizam; Yusuf, Muhammad; Maulana, Aga; Marwan, Marwan; Dharma, Dian Budi; Idroes, Rinaldi
Infolitika Journal of Data Science Vol. 2 No. 1 (2024): May 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v2i1.184

Abstract

As the global energy landscape shifts towards sustainable sources, geothermal energy emerges as a pivotal renewable resource, particularly in regions with abundant geothermal potential like Indonesia. This study focuses on Mount Seulawah in Aceh Province, a region rich in geothermal resources, to optimize the selection of geothermal power plant (GPP) sites using the Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) method. Our approach integrates environmental, technical, and accessibility criteria, including distance to settlements, land slope, proximity to fault lines and heat sources, and road access. By employing a structured decision matrix and applying MOORA, we systematically evaluated and ranked potential sites based on their suitability for GPP development. The results highlight the site at Ie Brôuk as the most optimal due to its minimal environmental impact and superior geological and accessibility conditions. This study not only contributes to the strategic deployment of geothermal resources in Indonesia but also provides a replicable model for other regions with similar geothermal potentials, emphasizing the importance of a balanced and informed approach to renewable energy site selection.
Sistem Identifikasi Pembicara Berbahasa Indonesia Menggunakan X-Vector Embedding Misbullah, Alim; Saifullah Sani, Muhammad; Husaini; Farsiah, Laina; Zahnur; Martiwi Sukiakhy, Kikye
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 11 No 2: April 2024
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.20241127866

Abstract

Penyemat pembicara adalah vektor yang terbukti efektif dalam merepresentasikan karakteristik pembicara sehingga menghasilkan akurasi yang tinggi dalam ranah pengenalan pembicara. Penelitian ini berfokus pada penerapan x-vectors sebagai penyemat pembicara pada sistem identifikasi pembicara berbahasa Indonesia yang menggunakan model speaker identification. Model dibangun dengan menggunakan dataset VoxCeleb sebagai data latih dan dataset INF19 sebagai data uji yang dikumpulkan dari suara mahasiswa dan mahasiswi Informatika Universitas Syiah Kuala angkatan 2019. Fitur-fitur yang digunakan diekstrak dari dataset audio dengan menggunakan dua jenis konfigurasi mel frequency cepstral coefficients (MFCC). Untuk membangun model, fitur-fitur diekstrak dengan menggunakan MFCC, dihitung voice activity detection (VAD), dilakukan augmentasi dan normalisasi fitur menggunakan cepstral mean and variance normalization (CMVN) serta dilakukan filtering. Sedangkan proses pengujian model hanya membutuhkan fitur-fitur yang diekstrak dengan menggunakan MFCC dan dihitung VAD. Selanjutnya, dibangun empat model dengan cara mengombinasikan dua jenis konfigurasi MFCC dan dua jenis arsitektur Deep Neural Network (DNN) yang memanfaatkan Time Delay Neural Network (TDNN). Model terbaik dipilih berdasarkan akurasi tertinggi yang dihitung menggunakan metrik equal error rate (EER) dan durasi ekstraksi x-vectors tersingkat dari keempat model. Nilai EER dari model yang terbaik untuk dataset VoxCeleb1 bagian test sebesar 3,51%, inf19_test_td sebesar 1,3%, dan inf19_test_tid sebesar 1,4%. Durasi ekstraksi x-vectors menggunakan model terbaik untuk dataset data train berdurasi 6 jam 42 menit 39 detik, VoxCeleb1 bagian test berdurasi 2 menit 24 detik, inf19_enroll berdurasi 18 detik, inf19_test_td berdurasi 25 detik, dan inf19_test_tid berdurasi 9 detik. Arsitektur DNN kedua dan konfigurasi MFCC kedua yang telah dirancang menghasilkan model yang lebih kecil, akurasi yang lebih baik terutama untuk dataset pembicara berbahasa Indonesia, dan durasi ekstraksi x-vectors yang lebih singkat.
Performance Assessment of Machine Learning and Transformer Models for Indonesian Multi-Label Hate Speech Detection Bagestra, Ricky; Misbullah, Alim; Zulfan, Zulfan; Rasudin, Rasudin; Farsiah, Laina; Nazhifah, Sri Azizah
Infolitika Journal of Data Science Vol. 2 No. 2 (2024): November 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v2i2.235

Abstract

Hate speech, characterized by language that incites discrimination, hostility, or violence against individuals or groups based on attributes such as race, religion, or gender, has become a critical issue on social media platforms. In Indonesia, unique linguistic complexities, such as slang, informal expressions, and code-switching, complicate its detection. This study evaluates the performance of Support Vector Machine (SVM), Naive Bayes, and IndoBERT models for multi-label hate speech detection on a dataset of 13,169 annotated Indonesian tweets. The results show that IndoBERT outperforms SVM and Naive Bayes across all metrics, achieving an accuracy of 93%, F1-score of 91%, precision of 91%, and recall of 91%. IndoBERT's contextual embeddings effectively capture nuanced relationships and complex linguistic patterns, offering superior performance in comparison to traditional methods. The study addresses dataset imbalance using BERT-based data augmentation, leading to significant metric improvements, particularly for SVM and Naive Bayes. Preprocessing steps proved essential in standardizing the dataset for effective model training. This research underscores IndoBERT's potential for advancing hate speech detection in non-English, low-resource languages. The findings contribute to the development of scalable, language-specific solutions for managing harmful online content, promoting safer and more inclusive digital environments.
Analisis Performa Segmentasi Citra MRI Tumor Otak dengan Arsitektur U-Net dan Res-UNet Misbullah, Alim; Mursyida, Waliam; Farsiah, Laina; Nazaruddin, Nazaruddin; Sukiakhy, Kikye Martiwi; Husaini, Husaini; Basrul, Basrul
J-SIGN (Journal of Informatics, Information System, and Artificial Intelligence) Vol 2, No 02 (2024): November
Publisher : Department of Informatics, Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/j-sign.v2i02.41358

Abstract

Diagnosis tumor otak melalui MRI menghadapi tantangan akibat keterbatasan dalam visualisasi morfologi, lokasi, dan batas-batas tumor. Format MRI yang biasanya dua dimensi memerlukan interpretasi manual oleh radiolog, yang meningkatkan risiko kesalahan manusia. Untuk meningkatkan akurasi segmentasi MRI, pendekatan pembelajaran mendalam seperti Convolutional Neural Networks (CNN) telah diterapkan untuk menyoroti area-area penting. Studi ini membandingkan dua arsitektur CNN, U-Net dan Res-UNet, untuk segmentasi tumor otak menggunakan dataset Brain Tumor Segmentation Challenge (BraTS) 2020. Kedua model dilatih dengan pengaturan yang serupa dan dievaluasi berdasarkan kemampuannya mengidentifikasi area kunci, termasuk inti tumor, edema, dan area tumor yang mengalami peningkatan. Model ini menggunakan optimizer Adam dan fungsi loss categorical crossentropy, dengan metrik evaluasi termasuk akurasi. Hasil menunjukkan bahwa U-Net mencapai performa optimal pada 35 epoch dengan ukuran batch 64 dan learning rate 0,001, menghasilkan nilai loss terendah (0,0140) dan akurasi tertinggi (99,5%). Meskipun Res-UNet juga mencapai akurasi tinggi (99,3%), nilai loss yang lebih tinggi menunjukkan bahwa model ini kurang efektif dibandingkan U-Net.
Comparison of Support Vector Machine and Random Forest Methods on Sentinel-2A Imagery for Land Cover Identification in Banda Aceh City Using Google Earth Engine Safira; Amiren, Muslim; Nazhifah, Sri Azizah; Rusdi, Muhammad; Nizamuddin; Misbullah, Alim
The Indonesian Journal of Computer Science Vol. 13 No. 6 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i6.4510

Abstract

Land cover is a physical feature of the earth that illustrates the relationship between natural processes and social processes. Over time, there has been a lot of land conversion, where initially open land is now built-up land. This is due to the large-scale development in Banda Aceh City. Therefore, this study aims to compare the performance of two classification methods, namely using Support Vector Machine (SVM) and Random Forest in identifying land cover in Banda Aceh City using Sentinel-2A imagery via the Google Earth Engine platform. As for data recording, it starts from January 1 to December 31, 2023. There are 4 classes used in this study, namely vegetation, water bodies, built-up land, and open land. The classification results show that the Support Vector Machine and Random Forest methods have been successfully applied to identifying land cover in Banda Aceh City using Sentinel-2A imagery. The accuracy results show that the Support Vector Machine method has a higher accuracy value of 90.5% compared to the Random Forest method of 85.7%.
A Threshold-based Cloud Resource Allocation Framework with Quality of Services Considerations Husaini, Husaini; Misbullah, Alim; Farsiah, Laina
Transcendent Journal of Mathematics and Applications Vol 2, No 1 (2023)
Publisher : Syiah Kuala University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/tjoma.v2i1.31694

Abstract

Allocating the number of resources needed by cloud applications is very crucial concern in the cloud environment. If the resource allocation is not managed precisely, the cloud services may starve during the peak load time or waste the resources during the off-peak time. Auto-scaling mechanism is one approach used in cloud environment in which service providers can maintain the resources and reduce waste resources by automatically increasing or decreasing them when needed. It is still difficult to predict the client-side experience which later will cause in decreasing performance because of lacking computing instances. This paper focuses on allocating resources at the application level for the efficient resource utilization and presents a novel cloud resource management framework. The proposed system monitored the end-users response time directly from client-side. Several thresholds were defined with Quality of Services (QoS) considerations which include response time and error rates sampling to optimize the decision of reallocating the virtual resources. The results dynamically allocate the virtual resources among the cloud applications based on their workload. Based on the experimental results, the recommendation threshold is 0.6 for the cloud system, as it can improve performance while minimizing costs.
SISTEM REKOMENDASI PEMILIHAN PROGRAM STUDI BERBASIS HYBRID MENGGUNAKAN PENDEKATAN DEEP LEARNING Misbullah, Alim; Akbar, Mufid; Nazaruddin, Nazaruddin; Farsiah, Laina; Husaini, Husaini; Zulfan, Zulfan
CYBERSPACE: Jurnal Pendidikan Teknologi Informasi Vol 9, No 1 (2025)
Publisher : UIN Ar-Raniry

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22373/cj.v9i1.28944

Abstract

Education plays a critical role in shaping career decisions for the future. However, many students encounter difficulties in selecting suitable academic programs, often stemming from a lack of confidence in their ability to make appropriate decisions. Consequently, students may choose study programs that do not align with their personal characteristics. This study emphasizes the importance of providing comprehensive information about various academic programs offered in higher education and developing tools to assist prospective students in making informed decisions. To address these challenges, a recommendation system using Hybrid Filtering technology has been developed. The system integrates Content-Based Filtering and Collaborative Filtering methods within the TensorFlow Recommenders System (TFRS) framework. The study utilized data from undergraduate students of the Faculty of Mathematics and Natural Sciences (FMIPA) across seven academic programs. By employing 10 features representing students' interests and talents, the recommendation system generated accurate and tailored suggestions for study programs. The model was trained and evaluated using both real and augmented (augmented) datasets with predefined hyperparameters. Results demonstrated that using only the real dataset achieved a Top-1 accuracy of 0.59 and a Top-5 accuracy of 0.97. When incorporating the augmented dataset, the Top-1 accuracy improved to 0.66, while the Top-5 accuracy reached 1.0. The findings reveal that combining real and augmented datasets enhances average accuracy by approximately 10% compared to using the real dataset alone. Additionally, the study program recommendations produced by the model showed significant improvement in quality. A web-based recommendation system utilizing the TFRS model was developed and positively evaluated by FMIPA students. User feedback indicated high satisfaction with the system's recommendations, demonstrating its effectiveness in guiding students toward suitable academic programs.
Penerapan Metode Forward Chaining untuk Sistem Pakar Diagnosis Penyakit Ginjal Berbasis Website Husaini; At-Tharfi’in, Khairunnisa; Misbullah, Alim; Zahnur
JSI: Jurnal Sistem Informasi (E-Journal) Vol 17 No 1 (2025): Vol 17, No 1 (2025)
Publisher : Jurusan Sistem Informasi Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/jsi.v17i1.191

Abstract

An expert system is used to replicate the knowledge and reasoning of an expert to assist in decision-making, diagnosis, prediction, and problem-solving in a specific field. Expert systems have been applied across various domains, including healthcare, such as for diagnosing kidney diseases. In this case, the web-based expert system for kidney disease diagnosis is designed to help analyze symptoms and provide an initial diagnosis based on the knowledge and rules of an expert. The system is designed to be utilized by the public, allowing users to input their symptoms, after which the system will provide a diagnosis. It is expected that this expert system can help the public detect kidney disorders early by applying expert knowledge integrated within the system. Another benefit of this system is that it makes it easier for users to perform self-diagnosis and detect potential kidney issues early based on the symptoms they experience. This expert system is developed using the forward chaining method, leveraging the Laravel framework and MySQL database. Forward chaining is a reasoning technique that starts by using available facts and then progresses through relevant premises to reach a conclusion. The use of this method ensures a systematic and accurate reasoning process for generating diagnoses or decisions based on the input information. Testing of the application shows that the developed expert system has successfully met expectations in helping the public accurately and easily identify kidney diseases. Additionally, the application of forward chaining allows the system to provide precise diagnoses based on the symptoms entered by the user, improving the ease of access to health information efficiently.
Detection of DNS Spoofing Attacks on Campus Networks Using LightGBM with Hybrid Feature Selection (SelectKBest + SHAP) Budiansyah, Arie; Candra, Rudi Arif; Ilham, Dirja Nur; Misbullah, Alim
Brilliance: Research of Artificial Intelligence Vol. 5 No. 1 (2025): Brilliance: Research of Artificial Intelligence, Article Research May 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i1.5962

Abstract

This study investigates the detection of Domain Name System over HTTPS (DoH) spoofing attacks utilizing the CIRA-CIC-DoHBrw-2020 dataset, which encompasses over 100,000 labeled DNS records categorized as either normal or malicious. Features such as packet timing, packet size, and TLS parameters are utilized for detection purposes. A systematic feature selection process is conducted utilizing the Elbow and Kneedle methods based on F-Score values derived from a built-in model evaluation. This method ensures that the top features are selected objectively and quantitatively, thereby enhancing the robustness of the model. The model is trained using the five most significant features, yielding exceptional performance metrics: a training time of just 0.5727 seconds, an inference time of 0.0157 seconds, and an inference latency of 0.0035 milliseconds per sample. Moreover, the model delivers an outstanding accuracy of 0.9995, an F1-Score of 0.9995, and an AUC-ROC of 1.0000, reflecting near-perfect detection capabilities. The classification report reveals a balanced distribution of precision, recall, and F1-Scores of 1.00 across both normal and malicious classes, based on a test sample of 14,974 entries. The Elbow plot visually confirms the optimal number of features utilized, while the SHAP beeswarm plot provides insights into how each selected feature contributes to the model’s predictions, facilitating interpretability. Additionally, the confusion matrix corroborates the model's reliability, showcasing that nearly all samples were accurately classified. The results demonstrate that the proposed methodology significantly enhances the effectiveness of DNS spoofing detection, offering a promising avenue for securing DNS over HTTPS communications.
Co-Authors . Zulfan Afidh, Razief Perucha Fauzie Aflah, Tsani Akbar, Mufid Akhyar, Fikrul Arie Budiansyah Asma Liza, Lia At-Tharfi’in, Khairunnisa Azhar, Fauzul Bagestra, Ricky Basrul, Basrul Candra, Rudi Arif Deviani, Rini Dharma, Dian Budi Essy Harnelly Farsiah, Laina Ghazi Mauer Idroes Hanifiah, Jessy Husaini Husaini Husaini Husaini Husaini Ilham, Dirja Nur Irvanizam, Irvanizam Kusumo, Fitranto Lala, Andi Martiwi Sukiakhy, Kikye Marwan Marwan Maulana, Aga Muhammad Irfan Muhammad Rusdi Muhammad Subianto Muhammad Yusuf Mursyida, Waliam MUSLIM MUSLIM Muzailin, Muzailin N. Nazaruddin Nazaruddin Nazhifah, Sri Azizah Nizamuddin Rahman, Isra Farliadi Rasudin, Rasudin Rinaldi Idroes SAFIRA Saifullah Sani, Muhammad Saputra, Kurnia sukiakhy, kikye martiwi Teuku Rizky Noviandy Wira Dharma Yunardi, Dalila Husna Zahnur Zulfan