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All Journal Pythagoras: Jurnal Matematika dan Pendidikan Matematika MEKTRIK Jurnal Pembelajaran Berpikir Matematika Pharmauho: Jurnal Farmasi, Sains, dan Kesehatan Jurnal Sains dan Teknologi Pangan EDULITE: Journal of English Education, Literature and Culture Sinkron : Jurnal dan Penelitian Teknik Informatika IKRA-ITH ABDIMAS JURTEKSI Jurnal Riset Informatika Berkala Penelitian Agronomi JATI (Jurnal Mahasiswa Teknik Informatika) BERNAS: Jurnal Pengabdian Kepada Masyarakat Jurnal Guru Peneliti Profesional Journal of Applied Data Sciences Jurnal Pengabdian Masyarakat Ilmu Terapan (JPMIT) eScience Humanity Journal Jurnal Pengabdian Masyarakat Singa Podium (JPMSIPO) PORTAL ABDIMAS Jurnal Pengabdian Masyarakat Media Hukum Indonesia (MHI) ANIMATOR Bakti Cendekia (Jurnal Ilmiah Multi Disiplin) Zad Al-Ummah: Jurnal Pengabdian Masyarakat
Asrul Sani
Faculty of Mathematics and Natural Sciences, Department of Mathematics, Universitas Haluoleo. Jl. HEA Mokodampit, Kendari 93232
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Neuroscience Nursing Public Health Social Sciences Veterinary Other

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KLASIFIKASI KASUS CACAR MONYET DI INDONESIA TAHUN 2022-2024 MENGGUNAKAN ALGORITMA NAIVE BAYES Farouk Adel, Ahmad; Sani, Asrul
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 3 (2025): JATI Vol. 9 No. 3
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i3.13556

Abstract

Penyakit cacar monyet merupakan salah satu penyakit menular yang kasusnya semakin meningkat di berbagai daerah. Untuk membantu dalam proses klasifikasi tingkat keparahan kasus, penelitian ini menerapkan algoritma Naïve Bayes dalam menganalisis data kasus cacar monyet di Indonesia tahun 2022–2024. Metode inidipilih karena kemampuannya dalam menangani data dengan atribut yang saling independen serta keefektifannya dalam klasifikasi. Penelitian ini menggunakan dataset yang telah dikategorikan ke dalam tiga kelas: rendah, sedang, dan tinggi. Data diolah menggunakan RapidMiner, dengan pembagian rasio data latih dan data uji yang bervariasi (50:50, 60:40, dan 70:30). Hasil evaluasi menunjukkan bahwa model dengan rasio 50:50 memberikan akurasi terbaik dibandingkan dengan rasio lainnya. Selain itu, metrik evaluasi seperti precision, recall, dan f1-score menunjukkan performa yang baik dalam mengklasifikasikan setiap kategori kasus. Berdasarkan hasil penelitian ini, dapat disimpulkan bahwa algoritma Naïve Bayes memiliki tingkat akurasi yang tinggi dalam mengklasifikasikan kasus cacar monyet. Temuan ini diharapkan dapat membantu instansi kesehatan dalam menentukan prioritas penanganan dan pencegahan terhadap kasus cacar monyet di Indonesia.
Pembelajaran Al-Qur`an Dengan Metode Talqin Di Masjid Abdullah bin Mas`ud dan Masjid Miftahul Hidayah Cibodas Rabbani, Muhammad Aqil; Dhiyaudin; Habibun; Sani, Asrul; Firmansyah, Hanif
Zad Al-Ummah: Jurnal Pengabdian Masyarakat Vol. 1 No. 2 (2023): Zad Al-Ummah: Jurnal Pengabdian Masyarakat
Publisher : Pusat Penelitian dan Pengabdian Masyarakat STIQ ZAD Cianjur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55759/zau.v1i2.10

Abstract

Qur'anic learning with the Talqin method is Qur'anic learning in which an Ustadz or Qur'anic teacher dictates the recitation of the Qur'an to the participants in accordance with the rules of tajweed, Waqaf rules, and Ibtida. This article aims to describe the effectiveness of learning the Qur'an with the Talqin method. This service reveals the effectiveness of learning the Qur'an using the Talqin method to TPQ students at the Abdullah bin Mas`ud Mosque and the Miftahul Hidayah Cibodas Cianjur Mosque. The results of this service show the effectiveness of the Talqin method for Qur'an learners at the Abdullah bin Mas`ud Mosque and the Miftahul Hidayah Cibodas Mosque.
ANALISIS RANTAI MARKOV TERHADAP PERPINDAHAN PENGGUNAAN MEREK HANDPHONE Putri, Widya; Sani, Asrul; Aswani
Bakti Cendekia Vol. 2 No. 1 (2025): JANUARI - JUNI
Publisher : Ikatan Cendekiawan Hindu Indonesia Regional Sulawesi Tenggara

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Analisis rantai Markov adalah suatu metode yang mengkaji karakteristik suatu variabel pada masa kini berdasarkan karakteristiknya di masa lalu untuk memperkirakan karakteristik variabel tersebut di masa yang akan datang. Perpindahan merek (Brand Switching) adalah tindakan seorang konsumen yang mengubah suatu merek dari satu produk ke produk lainnya karena alasan tertentu. Tujuan penelitian ini adalah mengetahui model dan selesaian model matematika penggunaan merek handphone mahasiswa Jurusan Matematika Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Halu Oleo menggunakan analisis rantai Markov. Pembentukan model diawali dengan membentuk tabel perpindahan merek handphone yang selanjutnya dirumuskan ke dalam bentuk matriks peluang transisi. Hasilnya, keadaan steady state didapatkan pada langkah ke-6 atau ????6 dengan prediksi proporsi pengguna masing-masing merek handphone pada periode mendatang yaitu Samsung sebesar 28,42%, Iphone sebesar 16,47%, Vivo sebesar 16,51%, Oppo sebesar 11,16%, Xiaomi sebesar 11,38%, Realme sebesar 10,72%, Infinix sebesar 4,55%, dan Lainnya sebesar 0.54%. Simulasi numerik model yang dilakukan sejalan dengan analisis rantai Markov yang dilakukan.
MCDM-based Fire Risk Mapping with Geospatial Visualization and Blockchain Paays, Emmanuel Abet Rossi; Hindarto, Djarot; Sani, Asrul
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15436

Abstract

Forest fires are among the most destructive environmental disasters in Indonesia, causing long-term ecological damage, health problems, and economic disruption. Increasing occurrences driven by climate anomalies, land clearing, and vegetation dryness highlight the need for intelligent and data-driven risk monitoring systems. This study introduces a hybrid analytical framework that integrates Multi-Criteria Decision-Making (MCDM) with blockchain-based data management and geospatial visualization to identify forest fire risk levels. The proposed model combines the Analytic Hierarchy Process (AHP), Weighted Sum Model (WSM), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to evaluate multiple parameters, including temperature, humidity, rainfall, and the Normalized Difference Vegetation Index (NDVI). Environmental data were securely obtained from a private Ethereum blockchain using Ganache, Truffle, and MetaMask to ensure transparency, integrity, and immutability. Results were visualized through an interactive Leaflet.js interface, allowing real-time geospatial monitoring linked to blockchain transaction hashes. The AHP analysis revealed that temperature (0.36) and humidity (0.27) contributed 63% of the total decision weight, while TOPSIS identified high-risk zones consistent with historical records. Validation against BNPB data achieved 90.7% accuracy, confirming the model’s reliability. The integration of MCDM, GIS, and blockchain provides a transparent, decentralized, and verifiable approach for national-scale fire-risk management, enhancing the accuracy and credibility of environmental decision-making systems.
MCDM-Based Blockchain and Artificial Intelligence Integration for Earthquake Risk Recommendation System Widianto, Aditya; Sari, Ratih Titi Komala; Hindarto , Djarot; Sani, Asrul
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15437

Abstract

Indonesia is one of the countries with the highest earthquake vulnerability in the world because it is located at the meeting point of three major tectonic plates, namely Eurasia, Indo-Australia, and Pacific. The high risk of disaster requires a system that is capable of analyzing, predicting, and recommending earthquake-prone areas accurately, efficiently, and safely. This study aims to develop an earthquake risk recommendation system based on the integration of Artificial Intelligence (AI), Multi-Criteria Decision Making (MCDM), and Ethereum Blockchain. Earthquake data was obtained from Google Earth Engine (GEE) and geospatial data from the Geospatial Information Agency (BIG) and BMKG. The data is processed using AI algorithms for predictive analysis, then the MCDM methods of TOPSIS, and ELECTRE are applied to determine the priority of earthquake-prone areas based on a combination of seismic parameters, population density, infrastructure vulnerability, and distance to active faults. The analysis results are stored in a decentralized manner using the Ethereum Blockchain through smart contracts to ensure data integrity, security, and transparency. The research results show that the integration of AI–MCDM is capable of providing earthquake risk recommendations with high accuracy, while the application of blockchain ensures that the results cannot be manipulated. This system is expected to become a decision-making tool for disaster management agencies such as BMKG and BNPB in data-based earthquake risk mitigation.
Hybrid Artificial Intelligence–Blockchain Approach for Landslide Risk Classification and Recommendation Indriawan, Rizal; Komalasari, Ratih Titi; Hindarto, Djarot; Sani, Asrul
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15465

Abstract

Increased rainfall intensity, steep topography, and changes in land use in Indonesia, particularly in Java, such as Garut Regency, have increased the risk of landslides that have a widespread impact on public safety and environmental stability. This study proposes a Hybrid Artificial Intelligence and Blockchain approach to develop an accurate, secure, and transparent landslide risk classification and recommendation system. The model integrates three Multi-Criteria Decision Making (MCDM) methods, namely Analytic Hierarchy Process (AHP), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR). These three methods are used sequentially to determine criterion weights, calculate ideal solutions, and produce optimal compromise decisions based on geospatial factors. The dataset used consists of 766 geospatial observation data covering stability, rainfall, vegetation, river distance, slope, prediction, and ground truth parameters, obtained from satellite data and open geospatial repositories in the Java Island region. The research process included pre-processing, normalization, weighting analysis using AHP–TOPSIS–VIKOR, and integration of the results into the Ethereum Blockchain Smart Contract system with a Proof of Authority (PoA) consensus mechanism. The test results showed a 17.8% increase in classification accuracy and a 21.4% increase in data storage efficiency compared to conventional methods. This approach is expected to improve the reliability, security, and transparency of the analysis system and mitigate the risk of landslides based on smart technology in Indonesia.
Comparative Performance Evaluation of MobileNetV3 and ResNet50 for Forest Fire Image Classification Hidayat, Muhammad Rizky Amirullah; Hindarto, Djarot; Sani, Asrul
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15415

Abstract

Indonesia is one of the countries with a high incidence of forest and land fires (karhutla), especially during the dry season, thus requiring a fast and efficient early detection system. This study aims to compare the performance of two popular deep learning architectures, namely MobileNetV3 (Large and Small variants) and ResNet50, in forest fire image classification tasks using a transfer learning-based approach. This study emphasizes the comparison between accuracy and computational efficiency in a CPU-only environment, which represents real-world conditions of use in the field without GPU support. The dataset used is a combination of local field images from the Puncak area, Bogor, and a curated public forest fire dataset to ensure the model's generalization ability to diverse geographical conditions. The results of the experiment show that ResNet50 provides the highest accuracy with a training accuracy value of 0.677 and a validation accuracy of 0.647, but requires longer training and inference times. Meanwhile, MobileNetV3-Large and MobileNetV3-Small showed better computational efficiency, with only slightly lower accuracy (0.635 and 0.61) and high training stability. These findings confirm that lightweight models such as MobileNetV3 strike an optimal balance between accuracy, speed, and resource consumption, making them an ideal solution for implementing edge computing-based early detection systems. Overall, this research contributes by providing an empirical comparative analysis that can serve as a reference for selecting deep learning architectures for efficient and adaptive forest fire detection systems that are constrained by hardware limitations.
Integrating Blockchain with Neural Networks for Forest Fire Classification Yudistira, Hernan; Hindarto, Djarot; Sani, Asrul
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15421

Abstract

Forest fires represent one of the most severe environmental disasters, causing extensive ecological, social, and economic damage—particularly in tropical nations like Indonesia. This research introduces a hybrid framework that combines Blockchain and Neural Network technologies to classify forest fire images. The goal is not only to enhance detection precision but also to guarantee the integrity and security of experimental data. Two deep learning architectures, ResNet-50 and VGG-16, were implemented and evaluated to compare their effectiveness in differentiating fire from non-fire imagery. The dataset merges locally collected images from the Puncak area of Bogor, Indonesia, with the public FIRE dataset from Kaggle, thereby increasing model generalization. Model training utilized a transfer learning strategy, and its performance was assessed through four key indicators: accuracy, precision, recall, and F1-score. The findings demonstrate that VGG-16 achieved the most reliable outcomes, obtaining an accuracy of 0.91 and an F1-score of 0.87, outperforming ResNet-50, which reached 0.82 accuracy. All experimental data, including training and inference outputs, were stored using the InterPlanetary File System (IPFS), while each file’s Content Identifier (CID) and metadata were recorded in a blockchain-based smart contract to ensure transparency, verifiability, and data permanence. The study concludes that integrating blockchain with deep learning establishes a trustworthy and tamper-resistant framework for forest fire image classification. Future research may explore lighter CNN models and the fusion of IoT sensor data to enable adaptive and real-time fire detection.
Decision Support System for Outstanding Students’ Selection Using TOPSIS Suryani, Irma; Sani, Asrul; Budiyantara, Agus; Pusparini, Nur Nawaningtyas
Jurnal Riset Informatika Vol. 6 No. 2 (2024): March 2024
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v6i2.285

Abstract

In the school environment, determining outstanding students holds significant importance. High academic achievement among students and a low failure rate reflect the overall quality of education. Based on the interviews, it is known that the assessment process for outstanding students at school still needs to be revised, and the current decision-making system needs to consider other factors, resulting in suboptimal selection processes. To address this issue, implementing a Decision Support System (DSS) is necessary to assist the school in selecting the best students. DSS is an interactive system providing access to data and modelling information, designed to support decision-making in both structured and unstructured situations. This DSS will be designed using the Technique for Order of Preferences by Similarity to an Ideal Solution (TOPSIS) as the alternative ranking method. The final results indicate that using the TOPSIS method in this decision support system can improve efficiency and accuracy in selecting outstanding students in the school environment.
OPTIMALISASI PENUGASAN KERJA PADA DISTRIBUSI ROTI DENGAN METODE HUNGARIAN (STUDI KASUS: ROTI BAREN LIYA, WANGI-WANGI SELATAN): OPTIMALISASI PENUGASAN KERJA DENGAN METODE HUNGARIAN Rahmawati; Sani, Asrul; Budiman, Herdi; Kabil Djafar, Muhammad; La Gubu
Bakti Cendekia Vol. 1 No. 1 (2024): Bakti Cendekia
Publisher : Ikatan Cendekiawan Hindu Indonesia Regional Sulawesi Tenggara

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Pada penelitian ini diselidiki tentang optimalisasi penugasan pada pendistribusian roti dengan menerapkan metode hungarian dengan memperhatikan kasus pada pabrik roti Baren Liya, di daerah Wangi-Wangi Selatan. Pabrik tersebut mempekerjakan 6 (enam) karyawan yang ditugaskan mendistribusikan roti hasil produksi di 6 tempat berbeda. Total waktu yang diperlukan selama ini untuk mendistribusikan roti-roti tersebut adalah 116 menit. Setelah ditemukan jalur distribusi baru dengan menerapkan metode Hungarian, total waktu yang dibutuhkan adalah sekitar 105 menit. Berdasarkan jalur distribusi baru tersebut pendistribusian roti yang dilakukan oleh karyawan menjadi sangat optimal. Menurut jalur yang optimal tersebut karyawan pertama harus mengantar roti ke Wanci dengan waktu 20 menit, karyawan kedua harus mengantar roti ke Patuno dengan waktu 14 menit, karyawan yang ketiga harus mengantar ke Waha dengan waktu tempuh 15 menit, karyawan yang keempat mengantar roti ke Numana dengan waktu tempuh 20 menit, karyawan kelima mengantar ke Mandati dengan waktu tempuh 17 menit, dan karyawan yang keenam harus mengantar ke Mola dengan waktu 19 menit. Jadi terdapat efisiensi waktu sebanyak 11 menit.
Co-Authors A.A. Ketut Agung Cahyawan W AA Sudharmawan, AA Aang Subiyakto Aat Ruchiat Nugraha Abd. Rahman Ahlan Adriansyah, Ahmad Ardi Adrie Oktavio Agus Surono Alfian Amri, Miftachul Ananta, Dian Andi Hendra Andriani, Rina Ariana Azimah Arisma, Baiq Haemi Arman Aspadiah, Vica Aswani Azmi, Agus N Bafadal, Mentarry Baharuddin Baharuddin Bahriddin Abapihi Balogun, Naeem A Bambang Pramono Budilaksono, Sularso Budiman, Herdi Budiyantara, Agus Buruno, Yosep Heristyo Endro Deny Wiria Nugraha Dewanto Dewanto Dewi, Irma Shinta Dhiyaudin Diah Fatma Sjoraida DIRVAMENA BOER, DIRVAMENA Djafar, Muh. Kabil Djafar, Muhammad Kabil Dwi Yuniarto Edi Cahyono Ellina Rienovita Endah Fauziningrum, Endah Ery setiawan Fachrul Kurniawan Fadhliyah Malik Fahria Nadiryati Sadimantara Faizan, Ilmi Farouk Adel, Ahmad fatimah Fatimah Firmansyah, Hanif Fitria, Arie Fitriansyah, Ahmad Fristiohady, Adryan Guna, Bucky Wibawa Karya H, Herianto Habibun Hartawan, Muhammad S Hia, Septua Ginta Putra Hidayat, Muhammad Rizky Amirullah Hindarto , Djarot Hindarto, Djarot Huda, Muhammad Q Ida Usman Ilahi, Rahmat Indra Wahyu, Indra Indriawan, Rizal Irma Suryani Iswani, Fahrona Jaya Negara, I D G Jufra Jufra, Jufra Kabil Djafar, Muhammad Karimu, Laila Qodriyah Kritandani, Weny La Gubu La Pimpi M Tahir Mala, Hairul Megawati Melanza, Fattan Rezky Mira Nila Kusuma Dewi Muhammad Lutfi Muhammad Luthfi Muhammad Sudia Muhtar, Norma Muji, Muji Mukhsar . Mukhtar, Norma Murizar, Maldi Mustakim Mustamin Anggo Muzuni, Muzuni N, Nasria Nashrul Hakiem Novayanti, Novayanti Nur Akmal Nur Arfa Yanti Nur Hayati Nurhidayah P, Ipong Gawi Paays, Emmanuel Abet Rossi Perdana, Fitryan Dedi Pratama, M. Hengky Pusparini, Nur Nawaningtyas Puspita Sari Putri Utami, Irawati Putri, Widya Rabbani, Muhammad Aqil RAHMAT MULIADI, RAHMAT Rahmawati Ramadhani, Riki Ramadhani, Rizky Barkah Ratih Titi Komala Sari, Ratih Titi Komala Ratih Titi Komalasari Rembe, Elismayanti Rini Hamsidi Ririen Kusumawati RR. Ella Evrita Hestiandari Sabaria Sadimantara, Gusti Ray Saeruddin, Sahur Safrin, Safrin Sahidin . Samparadja, Hafiludin Sanjaya, Irfan Santosa, Tomi a Santosa, Tomi Apra Sapto Rahardjo Sapulete, Heppy Soleman, Soleman Solly Aryza Sopacua, Venty Sri Ambardini Suleman, Darwis Suryatno, Agung Suseno, Hendra B Via Yustitia Viva Arifin WAHYUNI Wayan Somayasa Wedha, bayu Yasa Widianto, Aditya Winarsih Winarsih Yudistira, Hernan