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All Journal Indonesian Journal of Electrical Engineering and Informatics (IJEEI) AdMathEduSt: Jurnal Ilmiah Mahasiswa Pendidikan Matematika Jurnal Informatika Upgris Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Indonesian Journal on Software Engineering (IJSE) Jurnal Komputer Terapan Jurnal Riset Informatika JSAI (Journal Scientific and Applied Informatics) Juriti Prima (Jurnal Ilmiah Teknik Industri Prima) bit-Tech JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Indonesia : Manajemen Informatika dan Komunikasi Jurnal Media Informatika Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Pariwisata Nusantara Journal of International Conference Proceedings Jurnal Indonesia Sosial Teknologi Jurnal Multidisiplin West Science Jurnal Manajemen Sistem Informasi Mutiara: Jurnal Penelitian dan Karya Ilmiah Indonesian Journal of Agriculture and Environmental Analytics
Joko Sutopo
Universitas Teknologi Yogyakarta
Religion Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Physics Public Health Social Sciences Other

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Development of Hidden Markov Model Based Hydrometeorological Disaster Prediction Model for More Effective Preparedness Joko Sutopo; Burhanuddin Mohd Aboobaider; Mustaqim Pabbajah; Juhansar Juhansar; Aprijanto Aprijanto; RB Hendri Kuswantoro
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 3 (2026): June 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i3.6359

Abstract

Hydrometeorological disasters, such as floods, are significant threats that require an accurate prediction system to improve preparedness and risk mitigation. This research aims to develop a Hidden Markov Model (HMM)-based hydrometeorological disaster prediction model by utilizing hydrological data from Kelambu Dam in Demak Regency. The data used includes water levels upstream (Level Up), downstream (Level Down), and water discharge (Q Serang), as well as information on flood events in the period 2022-2024. The methods applied include data collection and preprocessing, model training using the Baum-Welch algorithm, and performance evaluation with accuracy, precision, recall, and F1-score metrics. The results showed that the HMM model was able to identify hydrological change patterns and predict flood events with a high level of accuracy, reaching 94.29% at the best iteration. The performance evaluation also indicated that the model has a good balance between precision and recall, making it a potential tool in early warning systems. Thus, the implementation of this prediction model can improve community preparedness and support decision-making in hydrometeorological disaster management.
CONTEXTUAL ANOMALY DETECTION ON AWS CLOUDTRAIL LOGS USING AN UNSUPERVISED MACHINE LEARNING APPROACH: DETEKSI ANOMALI KONTEKSTUAL PADA LOG AWS CLOUDTRAIL MENGGUNAKAN PENDEKATAN UNSUPERVISED MACHINE LEARNING Adji Putra Noor Pamungkas; Joko Sutopo
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.8056

Abstract

Cloud audit logs contain rich behavioral signals that can be leveraged for security monitoring, yet effective anomaly detection remains challenging due to the absence of labeled data and the limited use of contextual information. This study investigates whether contextual feature engineering improves unsupervised anomaly detection in AWS CloudTrail logs by incorporating temporal attributes, per-entity historical activity, and geographic diversity. A controlled experimental design is employed to compare contextual and non-contextual feature representations using two representative unsupervised algorithms: Isolation Forest and Autoencoder. Both models are evaluated under identical datasets and configurations to ensure that performance differences are attributable solely to feature representation. The results indicate that contextual features significantly enhance detection performance across various synthetic threat scenarios. The average detection rate across all scenarios for the Isolation Forest improved from 4.62% to 85.62%, while the Autoencoder demonstrated an increase from an average of 20.24% to 97.26%. In addition, contextual representations consistently improve sensitivity to behaviorally meaningful anomalies that are not identifiable through global statistical rarity alone. Stability analysis using top-1 percent overlap across multiple random seeds reveals a trade-off between robustness and expressiveness, where contextual models exhibit lower but still operationally acceptable stability compared to non-contextual baselines. Overall, the findings demonstrate that contextual feature engineering is a critical component for effective unsupervised anomaly detection in cloud audit logs. By shifting the detection paradigm from global rarity to entity-level behavioral deviation, contextual modeling enables more accurate identification of security-relevant anomalies in AWS environments.  
Human Security and Coastal Diplomacy: A Comparative Study of Indonesia and Australia in Managing Tourist Safety Fiya Ainur Rohmatika; Joko Sutopo
Jurnal Multidisiplin West Science Vol 5 No 03 (2026): Jurnal Multidisiplin West Science
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/jmws.v5i03.3290

Abstract

Coastal tourism safety presents a diplomatic human security agenda, especially for Indonesia as an archipelagic country (≈99,093 km of coastline) and Australia with its mature coastal safety regime. This study aims to assess how the principle of human security is translated into tourism safety governance and how Indonesia-Australia coastal diplomacy produces soft power. The methods used are comparative studies based on policy and document analysis (BPS; BPS-DIY; National Coastal Safety Report 2024; Plan of Action 2025–2029), descriptive statistics of visits, and institutional assessments. The results show a strong recovery in Indonesian tourist arrivals: 4.05 million (2020), 1.56 million (2021), 5.89 million (2022), 11.68 million (2023), and 13.90 million (2024). At the regional level, DIY recorded 9,699 foreign tourists in May 2025 (up 35.94% m/m), with a cumulative total of 32,823 visits from January to May, while domestic tourist movements reached 3,547,415 trips. In Australia, the Surf Life Saving network comprises ≈316 clubs with >198,000 members, 558 patrol services, and 8,857 rescues in 2023/24; epidemiological literature estimates a backwash-related mortality rate of ≈0.11 per million visits. Comparisons indicate a gap in the degree of institutionalization: Australia displays consistent standards, proficiency tests, and exposure-based reporting, while Indonesia still varies between regions. In conclusion, coastal tourism safety is a manifestation of human security which, through coastal diplomacy, generates reputational gains (soft power). Recommendations include establishing national beach SOPs, exposure-based reporting, consistent certification, and multilingual risk communication as prerequisites for integrating human security into tourism governance
KOMPARASI MODEL DEEP LEARNING DALAM PREDIKSI HARGA SAHAM PADA SEKTOR PERTAMBANGAN INDONESIA Muhammad Rizki Ananda; Joko Sutopo
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 7 No 4 (2025): EDISI 26
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v7i4.6965

Abstract

Pertumbuhan investor baru di Indonesia meningkat pesat dalam beberapa tahun terakhir, terutama dari kalangan muda. Namun, keterbatasan pengetahuan mengenai analisis teknikal membuat banyak investor berisiko mengambil keputusan berdasarkan spekulasi, terutama pada saham sektor pertambangan yang dikenal memiliki volatilitas tinggi. Penelitian ini bertujuan membangun dan membandingkan performa tiga model deep learning seperti Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), dan Bidirectional GRU (BiGRU) dalam memprediksi harga penutupan saham subsektor logam. Data historis harian empat saham emiten pertambangan, yaitu BRMS, MDKA, ARCI, dan AMMN, dikumpulkan dari Yahoo Finance dengan rentang 2015–2024. Data melalui tahapan praproses, normalisasi Min-Max, serta pembentukan sekuensial menggunakan window size tujuh hari sebelum dibagi menjadi data latih (76,5%), validasi (8,5%), dan uji (15%). Evaluasi dilakukan menggunakan metrik RMSE, MAE, dan MSE. Hasil penelitian menunjukkan bahwa panjang data historis sangat memengaruhi tingkat akurasi model; saham dengan jumlah data lebih banyak (BRMS dan MDKA) menghasilkan prediksi yang lebih baik dibandingkan saham dengan data terbatas (ARCI dan AMMN). Secara keseluruhan, GRU menunjukkan performa paling stabil pada sebagian besar saham, sedangkan BiGRU dan LSTM unggul pada dataset tertentu. Temuan ini membuktikan bahwa deep learning dapat digunakan sebagai alat bantu prediksi harga saham, namun kualitas data historis menjadi faktor yang menentukan keberhasilan model.
STUDI LITERATUR AUDIT ISO 27001, COBIT, DAN ITIL PADA LAYANAN SISTEM INFORMASI Kristianto Pratama Dessan Putra; Joko Sutopo; Suhirman Suhirman
Jurnal Media Informatika Vol. 6 No. 6 (2025): Edisi Desember 2025
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jumin.v6i6.7446

Abstract

Proses audit memegang peranan penting dalam evaluasi suatu layanan informasi. Dengan adanya proses audit, celah-celah kecurangan ataupun kendala-kendala dapat diketahui dan melalui proses audit juga dapat memberikan rekomendasi perbaikan terhadap sistem informasi tersebut. Proses audit dapat memberikan perbaikan pada proses bisnis agar dapat berjalan lebih efektif dan efisien, serta minim dari risiko. Ada berbagai macam kerangka kerja audit sistem informasi, diantaranya ISO 27001, COBIT, dan ITIL dengan fokus dan karakteristik yang berbeda-beda. Menjadi hal penting untuk mengetahui perbedaan dari masing-masing kerangka kerja audit tersebut sehingga proses audit yang dipilih sesuatu dengan kebutuhan dari sistem informasi tersebut. Oleh karena itu, penelitian ini bertujuan untuk memberikan sumber referensi untuk memilih kerangka kerja audit sistem informasi yang tepat sesuai kebutuhan dari masing-masing pengguna.Hasil dari penelitian adalah ISO 27001 memiliki fokus pada keamanan dan perlindungan data sistem informasi, COBIT memiliki fokus pada tata kelola dan manajemen risiko sistem informasi, dan ITIL memiliki fokus pada peningkatan kualitas layanan dan keberlangsungan peningkatan sistem informasi. Selain itu, penelitian ini juga memberikan informasi latar belakang, kelebihan – kekurangan, serta langkah-langkah implementasi dari masing-masing kerangka kerja audit tersebut. Diharapkan penelitian ini dapat menjadi sumber referensi untuk menentukan kebutuhan audit dari masing-masing sistem informasi.
Analysis of Iot-Based Soil Moisture Data Visualization and Network Stability Effects on Precision Irrigation Monitoring Nurwahdaniah Maulida; Joko Sutopo
Indonesian Journal of Agriculture and Environmental Analytics Vol. 5 No. 1 (2026): January 2026
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/ijaea.v5i1.15882

Abstract

The Internet of Things (IoT) technology has enhanced precision irrigation monitoring by enabling real-time soil moisture measurement. However, the reliability of historical data visualization is strongly influenced by network stability between sensors and data servers. This study analyzes soil moisture trend visualization based on an IoT system and evaluates the effect of network stability on data consistency. Soil moisture data were obtained using a capacitive sensor integrated with an ESP32 microcontroller and transmitted via a Wi-Fi network. Data trends were visualized using time-series graphs, while network performance parameters, including latency, packet loss, and transmission delay, were analyzed. The results indicate that network instability causes data loss and irregular visualization patterns, which may affect irrigation monitoring accuracy. Therefore, improving network reliability is essential for sustainable precision agriculture systems.
Integrating Maqasid Syariah into Sustainable Halal Tourism in Southeast Asia: A Systematic Literature Review Fiya Ainur Rohmatika; Zainur Ridho; Moch Malik Dzulpikar; Joko Sutopo; Zidna Alaal Mujtaba; Andhika Nur Kurniawan
Jurnal Pariwisata Nusantara (JUWITA) Vol. 5 No. 1 (2026): Jurnal Pariwisata Nusantara
Publisher : PROGRAM STUDI PARIWISATA SYARAH, FAKULTAS EKONOMI DAN BISNIS ISLAM, UNIVERSITAS ISLAM NEGERI MATARAM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20414/juwita.v5i1.15369

Abstract

Purpose: This study aims to develop an integrative conceptual framework linking Sharia objectives (maqasid al-sharia) with sustainable halal tourism in Southeast Asia through research landscape mapping, identification of key findings, and a comprehensive literature synthesis. Method: This study employs a mixed-methods approach integrating bibliometric analysis with a systematic literature review based on the PRISMA 2020 protocol. A total of 62 Scopus-indexed articles from 2019 to 2025 were analyzed using VOSviewer software to identify major thematic clusters. Result: The bibliometric analysis revealed five main thematic clusters: halal tourism and Islamic marketing; tourist satisfaction and loyalty; sustainable tourism; Muslim tourists and destination development; and religiosity and local wisdom. The findings indicate that maqasid al-sharia serves as an integrative axis capable of bridging fragmentation among these clusters. The dynamics of implementing sustainable halal tourism in Malaysia, Brunei Darussalam, and Indonesia reveal diverse approaches grounded in maqasid al-sharia principles. Contribution: This study demonstrates that maqasid al-sharia can serve as a normative foundation for developing halal tourism that is ecologically, socially, and economically sustainable. The resulting integrative conceptual framework offers practical implications for policymakers, destination managers, and industry stakeholders in building a tourism ecosystem that is not only Sharia-compliant but also resilient, while contributing new theoretical knowledge by bridging the gap between Sharia principles and the global sustainability agenda.
Co-Authors Adji Putra Noor Pamungkas Agra Laksmi Maitri Agung Yuliyanto Nugroho Ahmad Taufiq Rohman Alvin Andrian Rivaldi Andhika Nur Kurniawan Anindya Nurindah Hidayati Annisa Rachman Aprijanto Aprijanto Aulia Nur Septiani Bambang Moertono Setiawan Burhanuddin Mohd Aboobaider Daffa Sholah Islamey Delfi Arviana Wulan Prastika Desi Ayu Rahmadani Fitria Shinta Devi Fiya Ainur Rohmatika Fransiscus Harri Dwi Kurniawan Man Hermanus Erwan Bali Loko Juhansar Andi Latief Kristianto Pratama Dessan Putra M. A. Burhanuddin Moch Malik Dzulpikar Mohd Khanapi Abd Ghani Muhammad Faishal Tsaqief Muhammad Habsyi Mubarak Muhammad Rizki Ananda Mustaqim Pabbajah Naufal Syafiq Fadillah Noviana Salsabila Nur Rohman Nurwahdaniah Maulida RB Hendri Kuswantoro Rebecca Angelina Silaen Ritzqy karina Sanusi Mulyo Widodo Silvan Abimanyu Sri Uszdevita Syardillah Pohan Suhirman Tundo, Tundo Zainur Ridho Zidna Alaal Mujtaba Zulhawati