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All Journal Jurnal Informatika Lontar Komputer: Jurnal Ilmiah Teknologi Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Informatika Jurnal Informatika Proceeding International Conference on Information Technology and Business Dharmakarya : Jurnal Aplikasi Ipteks Untuk Masyarakat Scientific Journal of Informatics International conference on Information Technology and Business (ICITB) jurnal Teknologi Informasi Magister Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal CoreIT Indonesian Journal of Artificial Intelligence and Data Mining Jurnal SOLMA Jurnal TAM (Technology Acceptance Model) JMM (Jurnal Masyarakat Mandiri) Prosiding Seminar Nasional Darmajaya Building of Informatics, Technology and Science Jurnal Aisyah : Jurnal Ilmu Kesehatan Journal of Computer Networks, Architecture and High Performance Computing Bioscientist : Jurnal Ilmiah Biologi Jurnal Pengabdian kepada Masyarakat JUSTIN (Jurnal Sistem dan Teknologi Informasi) NEAR: Jurnal Pengabdian kepada Masyarakat SIENNA Jurnal TAM (Technology Acceptance Model)
Y. Suhendro
Darmajaya Informatics And Business Institute
Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Library & Information Science Medicine & Pharmacology Nursing Public Health Social Sciences Transportation Veterinary Other

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Decision Support System for Aircraft Takeoff and Landing Using Mamdani Fuzzy Logic Based on Weather Parameters Armansyah, Armansyah; Irianto, Suhendro Yusuf
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7464

Abstract

Aviation safety is highly influenced by weather conditions, particularly during take-off and landing, necessitating an accurate feasibility assessment. Traditional manual methods rely on subjective judgment, making them prone to inconsistencies and errors. This study proposes a decision support system utilizing Mamdani fuzzy logic to process real-time meteorological data from the Radin Inten II station and assess take-off and landing feasibility. The system evaluates key weather parameters, including wind speed, wind direction, visibility, precipitation, and cloud height. Testing 31 data samples from BMKG, the system achieved an accuracy of 96.77%, with 30 out of 31 outputs matching standard aviation criteria. These results indicate that the system significantly improves decision-making reliability. The Mamdani fuzzy logic approach proves effective in interpreting complex weather data and generating consistent, data-driven recommendations to support safe aircraft operations.
Lithology Prediction Using Deep Learning Artificial Neural Network and Schlumberger Resistivity Inversion Data at Eastern Lampung Ramadhan, M Fitrah; Irianto, Suhendro Yusuf; Farduwin, Alhada
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 2 (2025): July 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i2.37652

Abstract

The Schlumberger geoelectric method has been extensively employed in earth resource exploration due to its capability to identify variations in subsurface resistivity. However, the manual interpretation of geoelectric data inversion results is often subjective and time-consuming. This study aims to automate the lithology identification process by utilizing deep learning techniques, particularly Artificial Neural Networks (ANN), based on the inverted resistivity parameters obtained through the IPI2Win software. The Schlumberger configuration geoelectric data were obtained from survey reports provided by the Ministry of Public Works and Housing (Kementerian Pekerjaan Umum dan Perumahan Rakyat/ PUPR), which conducted geoelectric measurements in East Lampung Regency, Lampung Province, Indonesia. The ANN algorithm demonstrated an average accuracy of 90% in predicting lithology based on resistivity patterns resulting from Schlumberger inversion. Outperforming Support Vectorr Machine (SVM) (87%) and XGBoost (88%). These results confirm the initial hypothesis that ANN can effectively capture the complex relationships between resistivity values and rock types. The present study proposes an integrated approach between geophysics and machine learning with ANN algorithms for lithology prediction based on Schlumberger configuration geophysical inversion data. The present study proposes an integrated approach between geophysics and machine learning with ANN algorithms for lithology prediction based on Schlumberger configuration geophysical inversion data.
COMPARATIVE ANALYSIS OF LUNG DISEASES FROM CHEST X-RAY IMAGES USING CONVOLUTIONAL NEURAL NETWORK AND SUPPORT VECTOR MACHINE Ningsiah, Ningsiah; Irianto, Suhendro Yusuf
Jurnal Aisyah : Jurnal Ilmu Kesehatan Vol 9, No 1 (2024): March 2024
Publisher : Universitas Aisyah Pringsewu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30604/jika.v9i1.2736

Abstract

The lungs are an important human organ in the human body, especially in the respiratory system. Another function of the lungs is to maintain stable body temperature, protect the body from dangerous substances, the nose is the sense of smell, but sometimes the lungs will experience conditions where they do not function normally. Chest x-ray images are the most well-known clinical method for the diagnosis of lung diseases. However, diagnosing lung diseases from chest x-ray images is a challenging task even for radiologists. This research proposes a system that can be used for comparative analysis of lung disease by applying the Convolutional Neural Network and Support Vector Machine methods. CNN is a method in the field of object recognition that has special layers, namely convolution layers and pooling layers which enable a good feature learning process. SVM is a comparative analysis method that relies on results from statistical learning theory to guarantee generalization performance. In this research there are 2 main processes, namely preprocessing and comparative analysis. There are 3 classes of disease for comparative analysis, namely Covid-19 disease, Tuberculosis disease, Pneumonia disease, and Normal disease. In this study, a comparison was also carried out between the classification carried out by CNN and SVM. The research data uses a chest X-ray image dataset. This research produces the best algorithm that is implemented to classify lung diseases from chest x-ray images.
Comparison of K-Nearest Neighbor, Naive Bayes, Random Forest Algorithms for Obesity Prediction Andani, Mia; Triloka, Joko; Irianto, Suhendro Yusuf; Nugroho, Handoyo Widi
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14478

Abstract

Obesity is a global health problem that continues to increase and has serious impacts on physical and mental health. This research aims to predict a person's obesity status based on certain attributes using the K-Nearest Neighbor (KNN), Naive Bayes, and Random Forest algorithms. The dataset used was taken from the Kaggle platform with 2,111 data and 16 attributes, including gender, age, weight, height, frequency of consumption of high-calorie foods, physical activity, and water and vegetable consumption patterns. The research process follows the data mining stages, including business understanding, data understanding, data preparation, modeling, evaluation, and documentation. Experiments were carried out using RapidMiner with a cross-validation technique using 10 folds to measure overall model performance. The research results show that the Random Forest algorithm performs best in predicting obesity status compared to K-NN and Naive Bayes. Model evaluation using accuracy, precision, recall, and F1-score metrics shows significant results in distinguishing obesity categories. It is hoped that this research can contribute to the development of a machine learning-based health prediction system that can be used to support decision-making in the prevention and management of obesity.
Evaluation of Information Security at the Radin Inten II Lampung Meteorological Station Using the KAMI Index Ardiansyah, Ardiansyah; Irianto, Suhendro Yusuf; Hasibuan, M. Said
Bioscientist : Jurnal Ilmiah Biologi Vol. 12 No. 2 (2024): December
Publisher : Department of Biology Education, FSTT, Mandalika University of Education, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33394/bioscientist.v12i2.12498

Abstract

Information security is a way to protect information assets from various potential threats. BMKG is a Non-Departmental Government Institution (LPND) in Indonesia whose main duties involve carrying out government duties in the fields of meteorology, climatology and geophysics. In connection with delivering information services appropriately and precisely to stakeholders, the Radin Inten II Lampung Meteorological Station needs to carry out an independent assessment in terms of security to evaluate the information system in each work unit, with the aim of understanding the level of readiness and maturity of information security. This research aims to measure the level of information security maturity at the Radin Inten II Lampung Meteorological Station. The analysis method used in this research is using the KAMI Index version 5.0 based on the ISO/IEC 27001:2022 standard. The research results indicate that the implementation of the ISO 27001:2022 standard in the information system of the Radin Inten II Lampung Meteorological Station is considered good. The total score obtained reached 591 based on analysis and questionnaires using the KAMI Index. With this score, the Radin Inten II Lampung Meteorological Station information system is categorized at level III, which indicates that some improvements are still needed.
Optimalisasi Media Sosial Sebagai Sarana Promosi Usaha Mikro Tanaman Aglaonema Sanusi, Anuar; Wibasuri, Anggalia; Irianto, Suhendro Yusuf; Herlina, Herlina; Sumbogo, Fransiskus Dibyo
Jurnal SOLMA Vol. 14 No. 3 (2025)
Publisher : Universitas Muhammadiyah Prof. DR. Hamka (UHAMKA Press)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22236/solma.v14i3.20997

Abstract

Pendahuluan: Perkembangan teknologi informasi telah mengubah media sosial menjadi sarana promosi efektif bagi UMKM dan menggantikan metode konvensional. Bagi usaha mikro tanaman Aglaonema, pemanfaatan platform seperti Instagram, Facebook, TikTok, dan WhatsApp Business menjadi strategi penting untuk mengatasi keterbatasan sumber daya dan pengetahuan digital. Studi ini bertujuan untuk mengidentifikasi peran media sosial, menganalisis strategi optimasi, serta mengungkap kendala yang dihadapi. Metode: Observasi, pembuatan dan optimalisasi konten digital, interaksi dan layanan konsumen, serta evaluasi. Hasil: Optimalisasi media sosial TikTok dan platform Shopee berhasil meningkatkan visibilitas, interaksi, dan penjualan produk Aglaonema. Konten kreatif di TikTok menarik lebih banyak konsumen, sementara pengelolaan Shopee yang profesional memperluas jangkauan pasar. Kesimpulan: Media sosial memiliki peran strategis dan multifungsi melalui promosi media sosial bagi usaha mikro tanaman Aglaonema.
Sentiment Analysis of Skincare Products Using Lexicon and Multinomial Naive Bayes on The Sociolla Website Rahmansyah, Ferdian; Sriyanto, Sriyanto; Lestari, Sri; Irianto, Suhendro Yusuf
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 4 (2025): Articles Research October 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i4.7048

Abstract

Global warming has triggered extreme weather that negatively affects skin health, including damage, premature aging, and increased risk of skin cancer, prompting the use of skincare products. E-commerce platforms like Sociolla simplify skincare purchases, but the abundance of choices and varying skin reactions make product selection challenging. This study aims to assist consumers in making smarter purchase decisions by analyzing user reviews using sentiment analysis with a lexicon-based approach and the Multinomial Naive Bayes algorithm to classify reviews as positive or negative. The process includes data collection, text preprocessing, model development, and performance evaluation. The results show that this method achieved an accuracy of 80,64%, demonstrating its effectiveness in helping consumers filter reviews and select appropriate skincare products.
Co-Authors - Nurjoko - Winarsih -, Meiliza -, Nisar A.A. Ketut Agung Cahyawan W Adimas, Adimas Agung Pradana Agus Rahardi Agus Rahardi, Agus Ahmad Rofi’i Ali, Rionaldi Andani, Mia Andreas Perdana anggalia wibasuri Anuar Sanusi Apri Ramadhan, Apri ARDIANSYAH ARDIANSYAH Arman Suryadi Karim, Arman Suryadi Armansyah Armansyah Aziz, RZ. Abdul Dian Sari Dona Yuliawati Dwi Handoko Effendi, Mukhammad Khoirul Eko Hendrawan Endang Astitin Enrico Findley Farduwin, Alhada Fauzi, Chairani Fitria - Fitria - Fitria Fitria handoyo widi nugroho Hendra Kurniawan Herlina Herlina Herwanto, Riko Ida Ayu Puspita Sari Indera Irianto . Irwanto, Ika Krisdiyanto, Wawan Lukmanul Hakim M Said Hasibuan Muhammad Galih Ramaputra Muktiawan, Danang Ade Ningsiah Nisar - Nursiyanto Ochi Marshella Febriani Onno W. Purbo Rahmansyah, Ferdian Ramadhan, M Fitrah Rian Yunandar Ribut Yulianto Rio Kurniawan Roffi, Ahmad Safitri, Egi Septiawan, Yuda Sken Jian Tao Sri Karnila Sri Karnila Sri Karnila Sri Lestari Sriyanto Sriyanto Sriyanto Sriyanto Sumbogo, Fransiskus Dibyo Sushanty Saleh Triloka, Joko Vitriani, Yelfi Wahyu Nugroho Wenni Puspa Sari Yudiansyah, Lepi Astra