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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 NUSANTARA: Jurnal Pengabdian Kepada 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 Jurnal Pengabdian Masyarakat Tekno Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK)
Rusdah Rusdah
Fakultas Teknologi Informasi Universitas Budi Luhur
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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%.
Edukasi Kecerdasan Buatan Bidang Jaringan Komputer untuk Meningkatkan Kompetensi Digital Siswa SMK Lukas Umbu Zogara; Hesti Umiyati; Rusdah; Maulana Agung Saputro
Jurnal Pengabdian kepada Masyarakat TEKNO (JAM-TEKNO) Vol 7 No 1 (2026): Juni 2026
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/jamtekno.v7i1.7282

Abstract

Perkembangan teknologi Kecerdasan Buatan (Artificial Intelligence/AI) semakin berpengaruh dalam pengelolaan jaringan komputer modern. Namun, siswa SMK jurusan Teknik Komputer dan Jaringan (TKJ) masih memiliki pemahaman yang terbatas terhadap penerapan AI di bidang tersebut. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan literasi dan kompetensi digital siswa melalui edukasi tentang konsep dasar serta penerapan AI dalam jaringan komputer. Metode pelaksanaan dilakukan melalui seminar edukatif dan diskusi interaktif yang diikuti oleh 40 siswa SMK Tunas Harapan. Materi mencakup pengenalan konsep AI, peran AI dalam otomatisasi jaringan, serta contoh penerapannya di industri. Data diperoleh melalui observasi dan kuesioner sebelum dan sesudah kegiatan. Hasil menunjukkan peningkatan pemahaman siswa terhadap konsep AI sebesar 78%, serta peningkatan minat untuk mendalami penerapan AI dalam bidang jaringan komputer. Kegiatan ini berhasil menumbuhkan kesadaran pentingnya penguasaan teknologi cerdas di kalangan siswa SMK dan memperkuat kesiapan mereka menghadapi tantangan dunia kerja berbasis digital. Diperlukan kegiatan lanjutan berupa pelatihan praktik agar pemahaman siswa semakin aplikatif dan berorientasi industri.
XgBoost Hyper-Parameter Tuning Using Particle Swarm Optimization for Stock Price Forecasting Dwi Pebrianti; Haris Kurniawan; Luhur Bayuaji; Rusdah Rusdah
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i4.27712

Abstract

Investment in the capital market has become a lifestyle for millennials in Indonesia as seen from the increasing number of SID (Single Investor Identification) from 2.4 million in 2019 to 10.3 million in December 2022. The increase is due to various reasons, starting from the Covid-19 pandemic, which limited the space for social interaction and the easy way to invest in the capital market through various e-commerce platforms. These investors generally use fundamental and technical analysis to maximize profits and minimize the risk of loss in stock investment. These methods may lead to problem where subjectivity and different interpretation may appear in the process. Additionally, these methods are time consuming due to the need in the deep research on the financial statements, economic conditions and company reports. Machine learning by utilizing historical stock price data which is time-series data is one of the methods that can be used for the stock price forecasting. This paper proposed XGBoost optimized by Particle Swarm Optimization (PSO) for stock price forecasting. XGBoost is known for its ability to make predictions accurately and efficiently. PSO is used to optimize the hyper-parameter values of XGBoost. The results of optimizing the hyper-parameter of the XGBoost algorithm using the Particle Swarm Optimization (PSO) method achieved the best performance when compared with standard XGBoost, Long Short-Term Memory (LSTM), Support Vector Regression (SVR) and Random Forest. The results in RSME, MAE and MAPE shows the lowest values in the proposed method, which are, 0.0011, 0.0008, and 0.0772%, respectively. Meanwhile, the  reaches the highest value. It is seen that the PSO-optimized XGBoost is able to predict the stock price with a low error rate, and can be a promising model to be implemented for the stock price forecasting. This result shows the contribution of the proposed method.
Forecasting Tourism Visitor Numbers Using a Recurrent Neural Network with a Long Short-Term Memory Algorithm Ibnu Fallah Rosyadi; Nurul Arifin Subandi; 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.
Edukasi Perlindungan Data Pribadi dalam Penggunaan Aplikasi Perbankan dan Media Sosial bagi Warga RW.011 Petukangan Utara Jakarta Selatan Dewi Kusumaningsih; Sri Wahyuningsih; Rusdah Rusdah; Yulianawati Yulianawati; Devit Setiono; Yuni Kasmawati; Rizq Mas Galih Wibisono; Yoshua Alfia Agatha; Lutfi Daniah; Firdhan Happyanda
Nusantara: Jurnal Pengabdian kepada Masyarakat Vol. 6 No. 3 (2026): Agustus: NUSANTARA Jurnal Pengabdian Kepada Masyarakat
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/nusantara.v6i3.9831

Abstract

The increasing use of digital banking applications and social media has heightened the risk of personal data misuse due to limited public awareness of digital security. This community service program aimed to improve community knowledge and practical skills in protecting personal data when using digital banking applications and social media among residents of RW 011, Petukangan Utara, South Jakarta. The program was conducted on February 13, 2026, involving 35 participants consisting of neighborhood administrators, Family Welfare Movement (PKK) members, and youth organization representatives. The program adopted a Participatory Action Research (PAR) approach combined with a Community-Based Education strategy through needs assessment, participatory planning, educational sessions, demonstrations, mentoring, and evaluation. The results showed improved participant awareness of protecting one-time passwords (OTP), using stronger passwords, enabling two-factor authentication, adjusting social media privacy settings, and recognizing phishing attempts. In addition, a Digital Literacy WhatsApp Group was established to support continuous learning within the community.
Early Detection of Dengue Hemorrhagic Fever Using Patient Medical Data with Ensemble Learning Methods Achmad Saleh; Ridha Mukhtar; 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

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.
ANALISIS SENTIMEN PADA MEDIA SOSIAL TERHADAP LAYANAN SAMSAT DIGITAL NASIONAL DENGAN SUPPORT VECTOR MACHINE Anindya Sasi Kirana; Rusdah Rusdah; Ririt Roeswidiah; Ahmad Pudoli
IDEALIS : InDonEsiA journaL Information System Vol. 8 No. 1 (2025): Jurnal IDEALIS Januari 2025
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/idealis.v8i1.3276

Abstract

Motor vehicle users experience rapid growth every year. The increasing number of vehicles contributes to one of the state revenues: taxes. SAMSAT is a state institution with the authority to regulate motor vehicle tax (PKB). As technology develops, SAMSAT innovates through the SIGNAL application, which allows people to make motor vehicle tax payments safely via cell phone. Social media such as Instagram and X have great potential for collecting data to understand public reactions to the SIGNAL application. Comments on social media regarding the SIGNAL application raise pros and cons from the public; therefore, it is necessary to carry out sentiment analysis through a text mining approach using the Support Vector Machine (SVM) algorithm following the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology. This research was carried out through several stages: data collection, preprocessing, modeling with the Support Vector Machine (SVM), and evaluation with a confusion matrix. Data in the research were collected from Instagram social media comments from September 20, 2023, until. 16 April 2024 as many as 3,543 records and 1,335 comments on X's social media from 31 May 2023 until March 27, 2024, with the keyword "SIGNAL application". After the preprocessing stage, the data used was reduced to 3,911 because there were duplicate and irrelevant reviews. based on 3,911 data, it produced 773 positive comments, 1991 negative, and 1147 neutral comments. This research aims to identify public sentiment towards SIGNAL services via social media, such as Instagram. We prepared a dataset of two and three sentiment classes for research modeling needs. Based on the application of the model, a Support Vector Machine (SVM) with a linear kernel produces better scores than the Naïve Bayes and KNN models with accuracy values ​​of 0.88, precision of 0.88, recall of 0.81, and AUC of 0.92 using a 10-fold cross-validation on training data and test data.
Prediksi Non-Performing Loan untuk Analisis Pengajuan Kredit Menggunakan Seleksi Fitur dan Ensemble Methods David Jefri Aruan; Rusdah Rusdah; Ahmad Pudoli
IDEALIS : InDonEsiA journaL Information System Vol. 9 No. 2 (2026): Jurnal IDEALIS Juli 2026
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/idealis.v9i2.3851

Abstract

Non-Performing Loans (NPL) are a fundamental indicator of a financial institution's asset health, reflecting loans that fail to meet interest or principal payment obligations as agreed. A high NPL ratio negatively impacts a bank's financial performance, such as decreased profitability as measured by Return on Assets (ROA) and decreased liquidity. Bank Indonesia sets an NPL tolerance limit of 5% of total credit provided by banking financial institutions. Therefore, a predictive model is needed that can detect the possibility of customers experiencing NPLs early. This study aims to identify relevant factors in predicting NPLs and create an NPL prediction model based on these factors. The contribution of this study lies in combining the results of three feature selection techniques: Chi-Square, Mutual Information, and Random Forest feature importance, using the average score eliminated by the Recursive Feature Elimination technique. Several ensemble algorithms, namely Random Forest, XGBoost, Gradient Boosting, and LightGBM, were explored to produce the best-performing model. Then, hyperparameter tuning was performed on the best model. The Random Forest model produced the best performance, with 92.17% accuracy, 78.1% precision, 98.1% recall, and 95.5% AUC. Hyperparameter tuning was shown to improve recall, thus improving the model's ability to measure how much positive data (Current class) was successfully predicted by the model. The results of this study can assist management in making credit decisions. Thus, it is hoped that it can help reduce the number of NPL cases.
Co-Authors Abdulhakim Madiyoh Achmad Saleh Achmad Solichin Afrianto, Whisnu Febry Ahadti Puspa Sari Ahmad Pudoli Alfad Zebua, Vivid Kristiani Andi Andara Andi Rukmana Anidnya Putri Pradiptha Anindya Sasi Kirana Anita Diana Anubhakti, Dian Ary Maulana Pratama Aryabima, Muhammad Iqbal Bregastantyo, Brian Agni Brury Trya Sartana Budiyoko, Budiyoko David Jefri Aruan Deasy Aprilla Wulandari Deni Mahdiana Devit Setiono Dewi Kusumaningsih Diwi Apriana Dwi Achadiani Dwi Kristanto Dwi Pebrianti Eka Dewi Satriana Elfy Susanti Ernita Rahayu Fauzan, Muhammad Rafi Fildza Izzati Firdhan Happyanda Hari Soetanto Haris Kurniawan Hin, Law Li Humisar Hasugian Ibnu Fallah Rosyadi 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 Kusumaningsih, Dewi Lauw Li Hin Linda Ratna Sari Lis Suryadi, Lis Luhur Bayuaji Lukas Umbu Zogara Lutfi Daniah Mahesworo Langgeng Wicaksono Marimin , Maulana Agung Saputro Mawarni, Ajeng Citra Mehmet Sıtkı ā°lkay Mohammad Syafrullah Muhamad Satriadi Muhamad Sobirin Jamil Muhammad Fauzan Hadi Saputra Muhammad Rifqi Nurul Arifin Subandi Painem, Painem Patlisan, Patlisan 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 Ridha Mukhtar Ririt Roeswidiah Rizky Pradana, Rizky Rizq Mas Galih Wibisono Roeswidiah, Ririt Rohmad Atkha Ruwirohi, Jan Everhard Setyawan Widyarto Shintya Yulianti Sri Hanafi Sri Wahyuningsih Sri Wahyuningsih Sucinda Kardena Supardi Supardi Susi Widyawati Syarifuddin Abdullah , Andi Tri Annisa Hidayati Triana Anggraini Umiyati, Hesti Wahiddin Ishak Yoshua Alfia Agatha Yulianawati Yulianawati Yulianawati Yulianawati Yuliazmi, Yuliazmi Yuni Kasmawati Zaqi Kurniawan