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All Journal TEKNIK INFORMATIKA Jurnal Simetris Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi Informasi dan Ilmu Komputer Proceeding of the Electrical Engineering Computer Science and Informatics Faktor Exacta Indonesian Journal of Artificial Intelligence and Data Mining IKRA-ITH Informatika : Jurnal Komputer dan Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan TIJAB (The International Journal of Applied Business) Jurnal Sisfokom (Sistem Informasi dan Komputer) KACANEGARA Jurnal Pengabdian pada Masyarakat STRING (Satuan Tulisan Riset dan Inovasi Teknologi) Systematics Idealis : Indonesia Journal Information System SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal Teknik Informatika (JUTIF) Jurnal Abdimas Sangkabira Jurnal Abdimas Bina Bangsa Kresna: Jurnal Riset dan Pengabdian Masyarakat Bit (Fakultas Teknologi Informasi Universitas Budi Luhur) International Journal of Science and Society (IJSOC) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Prosiding Seminar Nasional Pengabdian Kepada Masyarakat JAMAIKA: Jurnal Abdi Masyarakat International Journal on Advanced Technology, Engineering, and Information System (IJATEIS) Journal Of Communication Education Telematika MKOM Artinara Jurnal Ticom: Technology of Information and Communication Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK)
Rusdah Rusdah
Fakultas Teknologi Informasi Universitas Budi Luhur
Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Enhancing Prediction of Treatment Duration in New Tuberculosis Cases: A Comprehensive Approach with Ensemble Methods and Medication Adherence Rusdah, Rusdah; Painem, Painem; Kusumaningsih, Dewi
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.2.4263

Abstract

Tuberculosis (TB) remains a significant global health problem, with treatment duration varying among patients. TB patients have difficulty following a long-term treatment regimen. After the final diagnosis is determined, it is necessary to know the predicted duration of treatment for a patient. By increasing patient compliance with taking medication, the percentage of TB patients will increase, and this can reduce cases of multi-drug resistant patients and dropouts. This study aims to build a prediction model for the duration of treatment for new cases of Pulmonary TB patients by adding medication compliance parameters using the ensemble method. The research methodology uses CRISP-DM. This study begins with identifying problems and objectives, collecting data, preprocessing and analyzing data, modeling, evaluating, and validating models. The results showed that adding medication compliance parameters can improve model performance. However, the results of model exploration with feature selection techniques and various ensemble methods have not shown good performance. The medication adherence parameters used in this study are the number of medications swallowed in Phase I and Anti-Tuberculosis drug compliance in Phase I. These parameters had never been used in previous studies. The prediction model can be used as an early warning for a patient. If a patient is predicted to have a treatment duration of more than six months, then the patient will receive stricter drug intake supervision. Thus, this proposed model is expected to help achieve the target of eliminating Tuberculosis in 2030 to reduce the death rate by 90% compared to 2019.
Peningkatan kompetensi algoritma dan pemrograman C/C++ bagi siswa dan siswi SMK YADIKA 4 Painem, Painem; Soetanto, Hari; Kristanto, Dwi; Solichin, Achmad; Rusdah, Rusdah
KACANEGARA Jurnal Pengabdian pada Masyarakat Vol 6, No 4 (2023): November
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/kacanegara.v6i4.1689

Abstract

Salah satu bentuk tridharma perguruan tinggi adalah pengabdian kepada masyarakat. Selain menyelenggarakan pendidikan dan penelitian, perguruan tinggi juga memiliki tanggung jawab untuk memberikan kontribusi yang nyata bagi masyarakat di sekitar mereka. Pelatihan pemrograman bahasa C pada SMK Yadika 4 merupakan salah satu kontribusi nyata perguruan tinggi bagi masyarakat sekitar. Pelatihan pemrograman C/C++ dan kompetensi algoritma menjadi hal yang penting bagi siswa/siswi SMK Yadika 4. Hal ini bertujuan untuk meningkatkan kualitas pendidikan dan kesiapan siswa/siswi dalam memasuki dunia kerja yang membutuhkan kemampuan pemrogramanPelatihan pemrograman C/C++ dan kompetensi algoritma menjadi hal yang penting bagi siswa/siswi SMK Yadika 4 serta membekali siswa/siswi dengan pengetahuan dan keterampilan dasar pemrograman C/C++ sehingga mereka dapat mengembangkan aplikasi sederhana. Selain itu, pelatihan ini akan meningkatkan kompetensi algoritma siswa/siswi dalam memecahkan masalah dan merancang solusi yang tepat menggunakan algoritma yang efektif. Hal ini bertujuan untuk meningkatkan kualitas pendidikan dan kesiapan siswa/siswi dalam memasuki dunia kerja yang membutuhkan kemampuan pemrograman. Dalam pelatihan ini, siswa/siswi akan diberikan pemahaman dan latihan tentang konsep dasar pemrograman C/C++ dan kompetensi algoritma. Pelatihan ini akan meliputi pembelajaran teori dan juga praktek pengembangan program, di mana siswa/siswi akan belajar mengenai sintaks dasar, variabel, tipe data, operator, penggunaan loop dan kondisi, fungsi, dan lain sebagainya. Dengan meningkatnya kompetensi siswa/siswi dalam pemrograman C/C++ dan algoritma, diharapkan SMK Yadika 4 dapat melahirkan lulusan-lulusan yang siap dan mampu berkontribusi dalam industri teknologi informasi di masa depan.
Prediction of Graduation for Students at the ISB Atma Luhur Faculty of Information Technology Using the C4.5 Algorithm Putri, Ine Widyaningrum Mustama; Rusdah, Rusdah; Suryadi, Lis; Anubhakti, Dian
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 12 No. 3 (2023): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i3.1731

Abstract

Higher Education is a level of education after secondary education which includes diploma programs, undergraduate programs, master programs, doctoral programs, professional programs, and specialist programs organized based on the culture of the Indonesian nation. Student graduation is one of the important factors to improve university accreditation. Students who graduate above 5 years and the number of students who drop out are important indicators in determining accreditation which then causes the difficulty of accrediting a college to rise. This research aims as an early warning for students who graduate on time and graduate late from the Faculty of Information Technology, Institute of Science and Business Atma Luhur using the C4.5 decision tree algorithm by implementing the Cross-Industry Standard Process for Data Mining (CRISP- DM) method. The initial data of this research amounted to 1,015 which was taken through a query in the database of the Atma Luhur Institute of Science and Business. However, the data that will be used becomes 694 after preprocessing due to the large number of record contents that do not have a graduation year, with a total of 641 graduates graduating on time and 53 graduates graduating late. Based on the application of the model using the C4.5 decision tree algorithm and the Confusion Matrix method, the accuracy is 93.94%, Recall is 98.59%, and Precision is 95.03%. So it can be concluded that the C4.5 decision tree algorithm is the most effective algorithm for predicting student graduation, because it has a high level of accuracy.
Classification of Coconut Fruit Quality Using The K-Nearest Neighbour (K-NN) Method Based on Feature Extraction: Color, Shape, and Texture Kardena, Sucinda; Izzati, Fildza; Rusdah, Rusdah
JURNAL TEKNIK INFORMATIKA Vol. 18 No. 1: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v18i1.41225

Abstract

In 2021, Indonesia was the world's largest coconut producer, with production reaching 17.1 million tons, according to the Food and Agriculture Organization (FAO). However, due to the long distribution time from farmers to consumers, the quality of coconuts often decreases, mainly due to manual classification. Coconuts that meet consumption standards are considered suitable, while coconuts that are overripe, damaged, or unripe are considered Non-standard. To overcome this problem, an automatic classification system was developed using machine learning with the K-Nearest Neighbor (K-NN) algorithm. The total required dataset is around 500, comprising 250 standard coconut datasets and 250 non-standard coconut datasets. The dataset was taken from coconut Images from Indragiri Hilir, Riau Province. Coconut features colour, shape, and texture.. The development process used the Cross Industry Standard Process for Data Mining (CRISP-DM). The evaluation used a confusion matrix .This study explores five training-test ratio data split scenarios of 90:10, 80:20, 70:30, 60:40, and 50:50. The highest accuracy, 96%, is achieved with a data split of 90:10 and a K value 5. Then, the K-NN model will be compared with other models,  for Support Vector Machine (SVM) with RBF kernel accuracy of 94%, SVM with Linear kernel of 90%, Random Forest with accuracy of 92%, and Convolutional Neural Network (CNN) with accuracy of 86%.
Forecasting Tourism Visitor Numbers Using a Recurrent Neural Network with a Long Short-Term Memory Algorithm Rosyadi, Ibnu Fallah; Subandi, Nurul Arifin; Rusdah, Rusdah
INTERNATIONAL JOURNAL ON ADVANCED TECHNOLOGY, ENGINEERING, AND INFORMATION SYSTEM Vol. 4 No. 3 (2025): AUGUST
Publisher : Transpublika Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55047/ijateis.v4i3.1881

Abstract

Accurate forecasting of visitor numbers is essential in tourism management to ensure service quality and visitor satisfaction, especially during peak seasons such as holidays and weekends. This study addresses the lack of a predictive tool at PT Taman Impian Jaya Ancol (TIJA), a major recreational destination in Indonesia, by developing a forecasting model for visitor numbers. The research utilized monthly time series data of visitor numbers from January 2012 to December 2022. A Deep Learning approach was applied using the Recurrent Neural Network (RNN) architecture with the Long Short-Term Memory (LSTM) algorithm. The dataset was split with an 80:20 ratio for training and testing, normalized using the RobustScaler technique, and optimized with the ADAM optimizer. The model achieved a minimum Mean Squared Error (MSE) of 0.3095 and a prediction accuracy of 94.85%. These results indicate that the LSTM model can effectively predict visitor trends. The findings are expected to support TIJA and other tourism operators in preparing resources and facilities in advance, improving operational planning, and enhancing the overall visitor experience.
Early Detection of Dengue Hemorrhagic Fever Using Patient Medical Data with Ensemble Learning Methods Saleh, Achmad; Mukhtar, Ridha; Rusdah, Rusdah
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 3 (2025): November 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i3.38088

Abstract

Dengue Hemorrhagic Fever (DHF) remains a major public health concern in Indonesia and worldwide, where delayed diagnosis increases the risk of severe complications and mortality. Conventional laboratory-based diagnostics are time-consuming and often less accessible in resource-limited healthcare settings. This study aims to develop an early detection model for DHF using only initial clinical symptoms and demographic data extracted from electronic medical records at RSUD Brigjend H. Hasan Basry Kandangan. A total of 649 patient records (352 DHF cases and 297 non-dengue) were analyzed using the CRISP-DM framework. Five ensemble learning algorithms Random Forest, Bagging, AdaBoost, and Gradient Boosted Tree were evaluated across 80:20, 70:30, and 60:40 data splits and validated using 5-fold and 10-fold cross-validation. Random Forest consistently delivered the best and most stable performance, achieving up to 90.00 % accuracy and 0.967 AUC in the 80:20 split and mean accuracies of 88.91 % (5-fold) and 88.29 % (10-fold) in cross-validation. Further hyperparameter tuning enhanced model stability and prevented overfitting. The findings confirm that initial clinical symptoms and demographic attributes can reliably identify DHF cases early, enabling faster and more affordable screening prior to laboratory confirmation. This machine learning based decision-support model has the potential to significantly improve early clinical management of dengue fever.
Co-Authors Abdulhakim Madiyoh Achmad Saleh Achmad Solichin Afrianto, Whisnu Febry Ahadti Puspa Sari Alfad Zebua, Vivid Kristiani Andi Andara Andi Rukmana Anidnya Putri Pradiptha Anita Diana Anubhakti, Dian Ary Maulana Pratama Aryabima, Muhammad Iqbal Bregastantyo, Brian Agni Brury Trya Sartana Budiyoko, Budiyoko Deasy Aprilla Wulandari Deni Mahdiana Devit Setiono Diwi Apriana Dwi Achadiani Dwi Kristanto Eka Dewi Satriana Elfy Susanti Ernita Rahayu Fauzan, Muhammad Rafi Fildza Izzati Hari Soetanto Haris Kurniawan, Haris Hin, Law Li Humisar Hasugian Ilham Akbar Muharrom Ilyas, Aldrin Nur Imam Halim Mursyidin Indah Puspasari Handayani Indra Nugraha Irawati, Riri Izzati, Fildza Joko Christian Chandra Joko Sutrisno Juliasari, Noni Kardena, Sucinda Kirana, Anindya Sasi Kusumaningsih, Dewi Lauw Li Hin Linda Ratna Sari Lis Suryadi, Lis Luhur Bayuaji, Luhur Mahesworo Langgeng Wicaksono Marimin , Mawarni, Ajeng Citra Mehmet Sıtkı ā°lkay Mohammad Syafrullah Muhamad Sobirin Jamil Muhammad Fauzan Hadi Saputra Muhammad Rifqi Mukhtar, Ridha Painem, Painem Patlisan, Patlisan Pebrianti, Dwi Prayoga, Adistiar Pudoli, Ahmad Purwanto Purwanto Putri, Ine Widyaningrum Mustama Raden Rahmad Rafi Naufal AlBasri Rahmat Fajar Rahmawati Alvira Rahmawati, Fadilla Salsabila Raissa, Benita Hasna Ratna Ujiandari Renaldi Setiawan Putra Rizky Pradana, Rizky Roeswidiah, Ririt Rohmad Atkha Rosyadi, Ibnu Fallah Ruwirohi, Jan Everhard Setyawan Widyarto Shintya Yulianti Sri Hanafi Sri Wahyuningsih Subandi, Nurul Arifin Sucinda Kardena Supardi Supardi Susi Widyawati Tri Annisa Hidayati Triana Anggraini Yulianawati Yulianawati Yulianawati Yulianawati Yuliazmi, Yuliazmi Zaqi Kurniawan