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All Journal MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) TELKOMNIKA (Telecommunication Computing Electronics and Control) Journal of Innovation and Applied Technology Journal of Environmental Engineering and Sustainable Technology Jurnal Teknologi Informasi dan Ilmu Komputer Habitat Jurnal Edukasi dan Penelitian Informatika (JEPIN) SMATIKA Journal of Information Technology and Computer Science Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer JAST : Jurnal Aplikasi Sains dan Teknologi Indonesian Journal of Applied Informatics International Journal of Engineering, Science and Information Technology J-Intech (Journal of Information and Technology) Journal of Innovative and Creativity IMPACTS: International Journal of Empowerment and Community Services Journal of Information Technology
Arief Andy Soebroto
Fakultas Ilmu Komputer , Universitas Brawijaya
Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry 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 Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Optimalisasi Prediksi Kasus COVID-19 di Indonesia: Perbandingan Teknik Validasi 80-20 Split dan Walk-Forward dengan ARIMA Divanda Arya Inasta Asrul; Arief Andy Soebroto
J-INTECH ( Journal of Information and Technology) Vol 12 No 02 (2024): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v12i02.1373

Abstract

This study presents a comparative analysis of the 80-20 split and walk-forward validation techniques for forecasting daily COVID-19 cases in Indonesia using the ARIMA model. Building on previous research, the ARIMA model has proven effective in various epidemiological contexts; however, this study highlights the critical importance of selecting the appropriate validation technique. The study uses data from January 3, 2020, to October 18, 2023, to develop a predictive model evaluated using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The findings indicate that the walk-forward validation technique outperforms the 80-20 split, with MAE of 137.32 and RMSE of 198.23, compared to the 80-20 split MAE of 4190.92 and RMSE of 4479.15. These results suggest that walk-forward validation provides more accurate and reliable predictions, particularly for dynamic and non-stationary data scenarios. This study underscores the significant impact of validation technique selection on ARIMA model performance, contributing new insights into forecasting methodologies in epidemiology.
Sistem Informasi Profil Kelompok Pertanian Terpadu Berbasis Web dengan Integrated Farming (Studi Kasus: Desa Dawuhan, Malang) Arief Andy Soebroto; Nurul Hidayat; Rizal Setya Perdana; Indriati Indriati; Hendra Darmawan; Raihan Fikri Brilliansyach; Mohammad Ibnu; Nadhira Nurannisa; M Azka Obila Vasya
J-INTECH ( Journal of Information and Technology) Vol 12 No 02 (2024): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v12i02.1501

Abstract

Dawuhan Village in Poncokusumo District, Malang Regency, is an evolving village with significant potential in the livestock sector. However, livestock data management in this village is still done manually, facing various challenges such as limited access, data integrity issues, and time-consuming processes. To address these issues, this research aims to develop a Web-Based Integrated Livestock Group Profile Information System. The primary objectives of this study are to improve accessibility, streamline the livestock data management process, and enhance data accuracy and security. The system is designed using the Next.js framework, chosen for its ease of use and security in implementing authentication and authorization, as well as its capability for future integration. The research results show that the developed system functions according to the requirements, providing a more efficient platform, reducing errors, and enhancing the user experience for farmers involved in data management. The implementation of this system is expected to improve operational efficiency and livestock data management in Dawuhan Village comprehensively.
Deep Learning Architecture Model for Iris Image Segmentation in Biometrics Arief Andy Soebroto; Wayan Firdaus Mahmudy; Nurul Hidayat; Rekyan Regasari Mardi Putri; Anto Satriyo Nugroho
IJAI (Indonesian Journal of Applied Informatics) Vol 9, No 2 (2025)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijai.v9i2.100566

Abstract

Abstrak : Teknologi biometrik memanfaatkan karakteristik fisik atau perilaku manusia untuk identifikasi dan verifikasi identitas, dengan salah satu implementasi paling signifikan adalah biometrik iris. Teknologi ini menggunakan pola unik pada iris mata untuk tujuan identifikasi yang aman dan andal, namun masih menghadapi tantangan dalam memastikan segmentasi citra yang konsisten. Penelitian ini berfokus pada pengembangan segmentasi citra iris menggunakan deep learning sebagai langkah krusial dalam proses identifikasi biometrik iris. Segmentasi citra bertujuan untuk memisahkan wilayah iris dari bagian mata lainnya, seperti pupil, sklera, dan kelopak mata, namun proses ini memerlukan pendekatan yang lebih canggih untuk mengatasi variasi citra. Penelitian ini mengimplementasikan arsitektur deep learning populer, yaitu DeepLabV3 dan U-Net, untuk segmentasi citra iris. Evaluasi performa dilakukan menggunakan metrik IoU Score, Accuracy, Precision, Recall, dan F1-Score. Hasil pengujian menunjukkan bahwa DeepLabV3 memberikan kinerja terbaik dengan IoU Score sebesar 0,918, Accuracy sebesar 0,993, Precision sebesar 0,962, Recall sebesar 0,952, dan F1-Score sebesar 0,957. Keunggulan DeepLabV3 terletak pada kemampuannya dalam melakukan ekstraksi fitur yang kompleks dan menangkap konteks informasi pada berbagai skala secara efektif. Temuan ini menggarisbawahi potensi besar penerapan deep learning dalam segmentasi citra iris untuk sistem biometrik. Dengan performa optimal yang dicapai oleh DeepLabV3, teknologi ini dapat diandalkan untuk meningkatkan akurasi dan efisiensi proses identifikasi biometrik, membuka peluang luas untuk pengembangan lebih lanjut dalam aplikasi keamanan berbasis iris.===================================================Abstract :Biometric technology is an innovation that uses human physical or behavioral characteristics for identity determination and verification with an aspect of its most significant implementations identified to be iris biometrics. The technology uses unique patterns in iris for secure and reliable identification purposes but certain challenges are encountered in ensuring consistent image segmentation. Therefore, this research focuses on developing iris image segmentation using deep learning as an important step in biometric identification process. Image segmentation aims to separate iris region from other parts of the eye, such as the pupil, sclera, and eyelids. However, the process requires a more sophisticated method to overcome image variations. This research implements popular deep learning architectures, DeepLabV3 and U-Net, for the segmentation. Subsequently, the performance of the models was evaluated based on the IoU Score, accuracy, precision, recall, and F1-score metrics. The results showed that DeepLabV3 provided the best performance with an IoU Score of 0.918, accuracy of 0.993, precision of 0.962, recall of 0.952, and F1-score of 0.957. The advantage of the model was associated with the ability to effectively extract complex features and capture information context at different scales. The observation was an indication of the significant potential possessed by deep learning applications in iris image segmentation for biometric systems. Moreover, the optimal performance achieved by DeepLabV3 showed the possibility of depending on the technology to improve the accuracy and efficiency of biometric identification process, opening up broad opportunities for further development in iris-based security applications.
The Poncokusumo Village Information System In The Context Of Moving Towards A Digital Village Arief Andy Soebroto; Agi Putra Kharisma; Diva Kurnianingtyas; Syakirah Dwi Anisa; Charles Eugene; Annisa Indah Fitriani
Journal of Innovative and Creativity Vol. 6 No. 1 (2026)
Publisher : Fakultas Ilmu Pendidikan Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/joecy.v6i1.4956

Abstract

Manual administrative processes in Poncokusumo Village have long been a source of inefficiency, resulting in common problems such as service delays, restricted public access to information, and difficulties with data archiving. To tackle these issues, this study set out to analyze the impact of the Sistem Informasi Manajemen Desa/Kelurahan (SIMDEK) on the effectiveness of village administration. The research employed the Community-Based Participatory Action Research (CBPAR) method, utilizing a combination of questionnaires and the Wilcoxon test to assess the understanding of the SIMDEK website among village officials and residents. The primary goal was to see if the system could genuinely improve administrative efficiency and transparency. The findings from the data analysis were compelling. The study's results demonstrated that the SIMDEK training had a significant positive effect on improving the understanding of village residents. This was statistically confirmed by an Asymp. Sig (2-tailed) value that was smaller than the significance level α, which is the standard measure for statistical significance. Based on these outcomes, the conclusion is clear: the implementation of SIMDEK can markedly improve the speed of services, the accuracy of data processing, and the transparency of information within Poncokusumo Village. This study holds significant implications, providing a strong case for village governments to expand the use of SIMDEK. Doing so is not just a technological upgrade; it represents a strategic and necessary step toward the broader digital transformation of public services, ensuring a more responsive and accountable local government for the community.
An Expert System for Early Risk Diagnosis of Breast Cancer Using Fuzzy Mamdani and Case-Based Reasoning Rumahorbo, Cicilia Angelica; Arief Andy Soebroto; Putra Pandu Adikara; Diah Prabawati Retnani
Journal of Information Technology and Computer Science Vol. 10 No. 3: Desember 2025
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2025103854

Abstract

Breast cancer remains one of the leading causes of morbidity and mortality among women worldwide, making early detection essential to improve treatment outcomes. However, early-stage breast cancer symptoms are often subjective and non-specific, which complicates initial risk assessment. This study proposes an expert system for early breast cancer risk diagnosis by integrating Fuzzy Mamdani and Case-Based Reasoning (CBR). The Fuzzy Mamdani method is employed as the primary inference mechanism to model uncertainty in symptoms and risk factors using linguistic rules, while CBR is utilized as a decision support component by leveraging similarities with previously validated clinical cases. The dataset consists of 150 patient records, of which 123 cases are used as the case base and 27 cases are employed for system evaluation. Experimental results show that the proposed system achieves an accuracy of 92.59% compared to expert judgments. These findings indicate that the integration of Fuzzy Mamdani and Case-Based Reasoning provides an interpretable and adaptive approach for early breast cancer risk assessment and has potential as a screening support tool.  
Social Media-Based Nature Tourism Village Marketing Management Training Setiawan, Ari; Endah Emiarti; Pretty Diawati; Vera Selviana Adoe; Arief Andy Soebroto; Romanda Annas Amrullah; Farida Mony; Bioni Sena; Nurasiah; Alfry Aristo Jansen Sinlae
IMPACTS: International Journal of Empowerment and Community Services Vol. 4 No. 1 (2025)
Publisher : Faculty of Economics Universitas Sarjanawiyata Tamansiswa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30738/impacts.v4i1.21184

Abstract

ABSTRACT Purpose ­ Ngudal Tourism Village in Tawangmangu, Central Java, possesses high potential for nature-based tourism due to its scenic landscapes and cultural richness. However, its digital presence and promotional strategies remain limited. This community service program aimed to enhance the digital marketing capacity of local tourism stakeholders through social media-based training. Methods - Fifteen participants—including homestay owners, youth representatives, culinary business actors, and village officials—were trained in content creation, platform management, audience engagement, and digital branding. The training adopted a participatory approach involving workshops, simulations, and mentoring. Result and discussions - As a result, participants demonstrated significant improvements in digital literacy, with post-training assessments showing a 47% increase in knowledge. The village launched official Instagram, Facebook, and YouTube accounts, achieving over 300 organic followers and substantial content engagement within one month. Economic impacts included increased homestay bookings and product inquiries via social media. A digital task force was formed to ensure sustainability. Conclusion - This program illustrates the potential of social media as a transformative tool for rural tourism promotion and recommends replicating such models in other under-promoted villages to foster inclusive digital development.
Implementasi Algoritma Random Forest Untuk Prediksi Churn Pada Pelanggan Retail Online Anam, Muhammad Haris Khoirul; Kurnianingtyas, Diva; Soebroto, Arief Andy
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 10 No 4 (2026): April 2026
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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

Abstract

Studi ini menganalisis prediksi pelanggan yang akan berhenti berlangganan (churn) di ritel online untuk membantu perusahaan mengembangkan strategi retensi yang lebih tepat sasaran. Data yang digunakan adalah dataset berisi 1.000 catatan pelanggan ritel online dengan 14 variabel prediktor dan 1 variabel target (Target_Churn). Untuk memastikan data siap untuk pemodelan, dilakukan langkah pra-pemrosesan, termasuk pengecekan kualitas data, transformasi fitur kategorikal dengan one-hot encoding, dan standardisasi fitur numerik. Dataset kemudian dibagi menjadi data pelatihan dan pengujian dengan rasio 70:30 menggunakan pengambilan stratified sampling. Model klasifikasi dibangun menggunakan algoritma Random Forest, dan optimasi hyperparameter dilakukan menggunakan GridSearchCV dengan validasi silang 5-fold untuk mendapatkan konfigurasi terbaik. Hasil pengujian menunjukkan bahwa model mencapai akurasi 48,33% dan nilai AUC-ROC 0,4825, yang menunjukkan bahwa kemampuannya untuk membedakan antara kelas churn dan non-churn masih rendah pada dataset yang digunakan. Namun, analisis kepentingan fitur mengungkapkan bahwa faktor-faktor yang terkait dengan transaksi dan kepuasan pelanggan cenderung memiliki dampak yang lebih besar daripada karakteristik demografis. Kesimpulan dari penelitian ini adalah bahwa model Random Forest belum dapat memberikan prediksi churn yang andal pada dataset ini, dan oleh karena itu, diperlukan data yang lebih representatif, penyertaan karakteristik perilaku, atau pengujian metode lain untuk meningkatkan kinerja.
Penerapan Model Arsitektur UNet untuk Peningkatan Resolusi Spasial Curah Hujan di Wilayah Pulau Jawa Berbasis Data MSWEP Putri, Nurulita Purnama; Saputro, Adhi Harmoko; Prasetya, Ratih; Soebroto, Arief Andy
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 13 No 1: Februari 2026
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2026131

Abstract

Pemodelan curah hujan dengan resolusi tinggi sangat penting untuk berbagai aplikasi meteorologi dan hidrologi, termasuk peringatan dini bencana, manajemen sumber daya air, dan perubahan iklim. Namun, data curah hujan dengan resolusi tinggi sering kali tidak tersedia atau terbatas dalam cakupan wilayah dan periode waktu tertentu. Oleh karena itu, metode downscaling berbasis deep learning dapat menjadi solusi untuk meningkatkan resolusi data curah hujan dengan akurasi yang lebih baik. Penelitian ini berfokus pada evaluasi arsitektur Convolutional Neural Network (CNN) yaitu U-Net dalam melakukan downscaling data curah hujan Multi-Source Weighted-Ensemble Precipitation (MSWEP) untuk wilayah Pulau Jawa. Tujuannya adalah untuk mengevaluasi efektivitas model U-Net dalam meningkatkan resolusi data curah hujan dari 0.2° ke 0.1°. Hasil evaluasi pada data testing menunjukkan bahwa U-Net memiliki performa lebih baik jika dibandingkan ResNet. U-Net menghasilkan RMSE 0.0168, MAE 0.0107, MSE 0.00028, dan R² 0.9919, sementara ResNet memiliki RMSE 0.0188, MAE 0.0122, MSE 0.00035, dan R² 0.9899. Dengan nilai kesalahan yang lebih kecil dan akurasi lebih tinggi, U-Net terbukti lebih unggul dalam menangkap pola data curah hujan. Penelitian ini menyimpulkan bahwa U-Net lebih unggul dalam meningkatkan resolusi data curah hujan dan lebih efisien dalam menangkap pola data, menjadikannya pilihan yang lebih baik untuk aplikasi downscaling curah hujan wilayah Pulau Jawa.   Abstract High-resolution rainfall modeling is crucial for various meteorological and hydrological applications, including disaster early warning systems, water resource management, and climate change analysis. However, high-resolution rainfall data are often unavailable or limited in spatial coverage and time periods. Therefore, deep learning-based downscaling methods can serve as a promising solution to enhance the resolution of rainfall data with improved accuracy. This study focuses on evaluating the performance of a Convolutional Neural Network (CNN) architecture, specifically U-Net, for downscaling Multi-Source Weighted-Ensemble Precipitation (MSWEP) data over the island of Java. The objective is to assess the effectiveness of the U-Net model in increasing the spatial resolution of rainfall data from 0.2° to 0.1°. Evaluation on the testing dataset shows that U-Net outperforms the ResNet model, achieving an RMSE of 0.0168, MAE of 0.0107, MSE of 0.00028, and R² of 0.9919, compared to ResNet’s RMSE of 0.0188, MAE of 0.0122, MSE of 0.00035, and R² of 0.9899. With lower error values and higher accuracy, U-Net demonstrates superior capability in capturing rainfall patterns. The findings of this study conclude that U-Net is more effective in enhancing rainfall data resolution and more efficient in learning spatial patterns, making it a better choice for rainfall downscaling applications over the Java region.
Co-Authors Achmad Arwan Achmad Ridok Adam Hendra Brata Ade Wija Nugraha Adhi Harmoko Saputro Adi Setyo Nugroho Admaja Dwi Herlambang Agi Putra Kharisma Agi Putra Kharisma, Agi Putra Agus Wahyu Widodo, Agus Wahyu Ahmad Afif Supianto Ahmad Mustafirudin Ahmad Shofi Nurur Rizal Aizul Faiz Iswafaza Alfarisi, Muhammad Asnin Alfry Aristo Jansen Sinlae Ali Akbar Alysha Ghea Arliana Amira Ibtisama Ana Kusuma Ardani Anam, Muhammad Haris Khoirul Andreas Tommy Christiawan Andri Wijaya Kusuma Annisa Indah Fitriani Anto Satriyo Nugroho Ari Setiawan Asrul Syawal Asrul, Divanda Arya Inasta Asus Maizar Suryanto H Austenita Pasca Aisyah Baghaz, Renanda DSP Bambang Gunadi Bioni Sena Brilliansyach, Raihan Fikri Caesar, Canny Amerilyse Candra Dewi Candra Dewi Catur Ari Setianto Charles Eugene Dama Yuliana Deby Putri Indraswari Denny Sagita Rusdianto Destyana Ellingga Pratiwi Destyana Ellingga Pratiwi Dhea Azahria Mawarni Dian Eka Ratnawati Diva Kurnianingtyas Divanda Arya Inasta Asrul Djoko Pramono Dwi Cindy Herta Turnip Dwi Puri Cemani Dzikrullah, Muhammad Aulia Fachruz Edy Santoso Eka Miyahil Uyun Eko Ari Setijono Marhendraputro Eko Arisetijono Elza Fadli Hadimulyo Endah Emiarti Enggar Septrinas Enggarsita Auliasin Eugenius Yosep Korsan N Evi Irhamillah Azza Faisal Roufa Rohman Faizatul Amalia Fajar Pradana Farida Mony Fauziah Mayasari Iskandar Febrianita Indah Perwitasari Fendy Yulianto Ferdy Wahyurianto Fildzah Amalia Galuh Mazenda Guruh Prayogi Willis Putra Habib Yafi Ardi Hanafi, Andy Hastian Bayu Hendra Darmawan Hendra Darmawan Herman Syantoso Himawan Sutanto I Gede Adi Brahman Nugraha I Putu Bagus Arya Pradnyana Ibnu, Mohammad Ibrahim Kusuma Imam Cholissodin Imam Cholissodin Imam Cholissodin Imam Cholissodin Imam Cholissodin Indra Ekaristio P Indriana Candra Dewi Indriati Indriati Indriati Indriati Indriati Indriati Ishak Panangian Sinaga Ismiarta Aknuranda Issa Arwani Issa Arwani Karmia Larissa Br Pandia Khoifah Inda Maula Khrisna Widhi Dewanto Krisna Wahyu Aji Kusuma Kurnianingtyas, Diva Lailatul Rizqi Ramadhani Lailil Muflikhah Laode Muhamad Fauzan Latifah Hanum Lily Montarcih Limantara M Azka Obila Vasya Mahdi Fiqia Hafis Maria Tenika Frestantiya Maria Tenika Frestantiya, Maria Tenika Maya Febrianita Moh. Sholichin Mohammad Ibnu Mohammad Imron Maulana Muh. Arif Rahman Muhammad Iqbal Kurniawan Muhammad Rois Al Haqq Muhammad Rouzikin Annur Muhammad Tanzil Furqon Muhammad Taruna Praja Utama Mutia Ayu Sabrina Nadhira Nurannisa Nadya Rahmasari Nadya Sylviani Nainggolan, Yohana Beatrice Niftah Fatiha Armin Niken Hendrakusma Wardani Nizar Rahman Kusworo Nurannisa, Nadhira Nurasiah Nuriya Fadilah Nurudin Santoso Nurul Faizah Nurul Faridah, Nurul Nurul Hidayat Nurul Hidayat Nurul Hidayat Nurul Hidayat Nurul Hidayat Odhia Yustika Putri Pretty Diawati Priyambadha, Bayu Putra Pandu Adikara Putri, Nurulita Purnama Raihan Fikri Brilliansyach Randy Cahya Wihandika Ratih Prasetya, Ratih Raymond Gunito Farandy Junior Rekyan Regasari Rekyan Regasari Mardi Putri Restia Dwi Oktavianing Tyas Retnani, Diah Prabawati Reynald Daffa Pahlevi Ridwan Fajar Widodo Rio Andika Dwiki Adhi Putra Rio Arifando Risda Nur Ainum Riski Ida Agustiyan Risqi Nur Ifansyah Rivaldy Raihan Syams Rizal Setya Perdana Rizal Setya Perdana Rizal Setya Perdana Romanda Annas Amrullah Rumahorbo, Cicilia Angelica Saiful Kirom, Muhammad Ihsan Santoso, Nurudin Sativandi Putra Satrio Agung Wicaksono Sitepu, Yosua Christiansen Stefan Levianto Sukamto, Anjas Pramono Surya Wirawan SUTRISNO Sutrisno Sutrisno Sutrisno, Sutrisno Syakirah Dwi Anisa Teddy Syach Pratama Thareq Ibrahim Tiara Rossa Diassananda Tryse Rezza Biantong Vasya, M Azka Obila Vera Selviana Adoe Vicky Virdus Vivien Fathuroya, Vivien Wayan Firdaus Mahmudy Wayan Firdaus Mahmudy Welly Purnomo Wijaya, Aldi Rahman Wildan Ziaulhaq Wildan Ziaulhaq Wildansyah Maulana Rahmat Yearra Taufan Ardy Rinaldy Yusril Iszha Eginata Zaien Bin Umar Alaydrus Ziya El Arief Ziya El Arief, Ziya El