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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 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 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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Penerapan Exponential Smoothing untuk Optimasi Linear Regression dalam Peramalan Perkara Lalu Lintas Ahadti Puspa Sari; Deni Mahdiana; Brury Trya Sartana; Rusdah Rusdah
KRESNA: Jurnal Riset dan Pengabdian Masyarakat Vol 3 No 2 (2023): Jurnal KRESNA November 2023
Publisher : DRPM Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/kresna.v3i2.91

Abstract

Pelanggaran lalu lintas merupakan salah satu masalah yang memicu terjadinya kecelakaan yang dapat menyebabkan adanya korban jiwa, luka ringan maupun luka berat. Sehingga pentingnya meramalkan perkara lalu lintas guna memberikan informasi kepada pemerintah dan pihak terkait mengenai kenaikan atau penurunan perkara lalu lintas yang terjadi pada bulan berikutnya, sehingga pemerintah dan pihak yang terkait dapat lebih serius dalam mengatasi kasus perkara lalu lintas di tahun berikutnya. Salah satu cara yang dapat dilakukan pengolahan data dengan menggunakan data mining. Dalam penelitian ini menggunakan peramalan atau forecasting untuk memperoleh gambaran mengenai nilai dari suatu data di masa mendatang. Metode Linear Regression mempunyai kelebihan diantaranya metode ini simple dan mudah dipahami tetapi memiliki hasil yang akurat, dan dapat memprediksi perkara lalu lintas dimasa mendatang berdasarkan nilai pelanggaran lalu lintas dimasa lampau. Maka pada penelitian ini, menggunakan algoritma Linear Regression yang dikembangkan dengan metode Exponential Smoothing guna meningkatkan kualitas data sehingga dapat meningkatkan akurasi prediksi pada Linear Regression dengan nilai Root Mean Square Error (RMSE) yang lebih baik. Kesimpulan yang didapatkan dari eksperimen yang dilakukan adalah bahwa memprediksi jumlah perkara lalu lintas menggunakan Split dataset dengan metode Linear Regression menghasilkan nilai RMSE sebesar 0.011 dan eksperimen menggunakan Split dataset dengan metode Linear Regression yang dikembangkan melalui metode Exponential Smoothing lebih akurat dengan nilai RMSE sebesar 0.002 dibanding metode Neural Network sebesar 0.003, metode Deep Learning sebesar 0.003 dan metode Support Vector Machine sebesar 0.916.
Digitalisasi Informasi Sekolah Menengah Kejuruan PGRI Larangan Berbasis Web Painem Painem; Hari Soetanto; Anidnya Putri Pradiptha; Joko Christian Chandra; Rusdah Rusdah
KRESNA: Jurnal Riset dan Pengabdian Masyarakat Vol 4 No 2 (2024): Jurnal KRESNA November 2024
Publisher : DRPM Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/kresna.v4i2.164

Abstract

Sekolah Menengah Kejuruan menghadapi tantangan dalam menyampaikan informasi secara efektif kepada siswa, guru, dan pihak terkait. Metode konvensional seperti pengumuman kertas sering kali kurang responsif terhadap kebutuhan komunitas sekolah yang dinamis, sehingga menimbulkan keterlambatan dan ketidakjelasan informasi. Untuk mengatasi masalah ini, digitalisasi informasi berbasis web diusulkan sebagai solusi untuk meningkatkan aksesibilitas dan efisiensi penyebaran informasi. Platform web yang dikembangkan akan menyediakan fitur-fitur seperti kalender akademik, informasi program kejuruan, serta pengumuman penting, dan didukung dengan pelatihan staf untuk pengelolaannya. Diharapkan solusi ini dapat memperkuat komunikasi, meningkatkan partisipasi kegiatan, dan menjadi model bagi institusi pendidikan lain dalam era digital ini.
PELATIHAN DESAIN KONTEN MEDIA SOSIAL DENGAN CANVA UNTUK MENINGKATKAN KREATIFITAS SISWA SMK TRIGUNA 1956 Kusumaningsih, Dewi; Rusdah, Rusdah; Everhard, Jan; Roeswidiah, Ririt; Syafrullah, Mohammad
Artinara Vol 4 No 1 (2025): Jurnal Artinara Februari 2025
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/artinara.v4i1.237

Abstract

The community service activity entitled “Graphic Design Training in an Effort to Equip SMK Triguna 1956 Students to Enter the World of Work Using Canva” aims to provide practical graphic design skills to SMK Triguna 1956 students to be better prepared to enter the world of work. Using the easily accessible Canva design platform, the training covered the basic introduction to graphic design, Canva's features, as well as the practice of creating designs such as posters, flyers, and social media content. The results of this training showed an increase in students' ability to create applicable and attractive designs. The participant satisfaction survey showed that the majority of students were satisfied and considered the skills acquired useful for their future careers. However, some participants expected further training to deepen the use of Canva features. This training was successful in providing graphic design skills that are relevant to industry needs.
Classification of Coconut Fruit Quality Using The K-Nearest Neighbour (K-NN) Method Based on Feature Extraction: Color, Shape, and Texture Sucinda Kardena; Fildza Izzati; 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%.
Deteksi Dini Penyakit Stroke pada Data Tidak Seimbang Menggunakan SMOTE dan Random Forest Aryabima, Muhammad Iqbal; Rusdah Rusdah; Roeswidiah, Ririt; Ahmad Pudoli
Jurnal Ticom: Technology of Information and Communication Vol 13 No 3 (2025): Jurnal Ticom-Mei 2025
Publisher : Asosiasi Pendidikan Tinggi Informatika dan Komputer Provinsi DKI Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70309/ticom.v13i3.156

Abstract

Loss of blood circulation to the brain causes a stroke, which is also known as a brain attack. In addition, blood clots are also the leading cause of stroke in the brain. Based on the WHO report, stroke is the leading cause of death in Indonesia in 2024, with a death rate reaching 131.8 per 100,000 population. This study aims to classify early detection of stroke disease by applying the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology using the Random Forest algorithm. The data used is public through the website www.kaggle.com, with a total of 4981 records consisting of 11 attributes. The data composition is unbalanced, with 4733 negative stroke data (95%) and 248 positive strokes (5%). Handling imbalanced data using the Synthetic Minority Oversampling Technique (SMOTE). The total data from SMOTE is 5981 records, with 4733 negative data and 1248 positive. After exploring several models, the best model was obtained using Random Forest with the SMOTE approach, producing an accuracy of 80.14%, AUC 0.836, recall 63.33%, and precision 11.42%.
A Forecasting Modeling of Imported Goods Release Waiting Time in Importer Logistics Operations Using Multiple Linear Regression Alfad Zebua, Vivid Kristiani; Rusdah, Rusdah
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9725

Abstract

Import activities play a critical role in international trade, directly affecting logistics efficiency and the competitiveness of importing companies. The process of releasing imported goods at ports often involves complex administrative procedures that can cause delays, leading to increased logistics costs. This study aims to predict the waiting time for the release of imported goods using a machine learning approach. A case study was conducted at PT. Sentra Sarana Logistic, a licensed customs broker responsible for import administration. The primary model applied was Multiple Linear Regression (MLR), and its performance was compared with Neural Network (NN) and Support Vector Machine (SVM) algorithms. Several influencing factors were considered, including tax payment time, inspection duration, and inspection status. Evaluation results indicate that the MLR model achieved the best performance, with an RMSE of 0.00653, MAE of 0.00544, and R-squared of 0.99999, demonstrating high prediction accuracy and a strong linear correlation. The SVM model yielded acceptable results (RMSE 0.74107, R-squared 0.98388) but underperformed compared to MLR. The NN model showed the lowest accuracy with RMSE 2.86599, MAE 2.38831, and R-squared 0.69510. The findings suggest that MLR, despite its simplicity, is highly effective for predicting waiting times in import logistics operations. This research not only offers a practical decision-support tool for importers but also contributes to the existing literature on machine learning applications in logistics operations and customs processing.
Predicting Early Lease Termination Risk in Jakarta Shopping Malls Using a SMOTE-Enhanced SVM Model for Financial Loss Prevention Syarifuddin Abdullah , Andi; Rusdah
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 11 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v11i3.30980

Abstract

The high incidence of early lease termination in shopping malls poses significant challenges to revenue generation, unit utilization, and the operational stability of commercial properties. The limitations of traditional management practices in identifying high-risk tenants early often result in financial losses and suboptimal asset allocation. To address this issue, this study developed a data-driven predictive model designed to identify the likelihood of early lease termination. The approach integrates the Support Vector Machine (SVM) algorithm with the Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance within the dataset. The model development followed the CRISP-DM methodology and utilized a historical dataset comprising 795 lease records from a major shopping mall in Jakarta, spanning the years 2015 to 2022. Through systematic data preprocessing, feature selection, and model optimization using grid search and cross-validation, the model achieved excellent classification performance: 93.10% accuracy, 90.50% precision, 96.40% recall, 93.30% F1-score, and 97.30% AUC. The findings demonstrate that the SMOTE–SVM combination consistently outperforms in detecting minority-class cases. A prototype system was also developed, enabling mall managers to predict tenant risk in real-time through an intuitive user interface. The contributions of this research are twofold. First, it presents a novel application of the SMOTE–SVM approach for addressing data imbalance in early lease termination prediction within the Indonesian commercial property sector an area that remains underexplored. Second, the study delivers a practical and deployable prototype system that enables real-time risk assessment for mall management, thereby bridging the gap between predictive modeling and operational decision-making. Overall, the proposed model offers a reliable and scalable predictive solution that can be adapted for risk management in other commercial property contexts, supporting a data-driven and proactive decision-making approach. However, it is important to note that the applicability of the proposed SMOTE–SVM model may face certain challenges when deployed in different commercial property contexts. Variations in tenant characteristics, market dynamics, economic conditions, and data availability across regions could impact model generalizability and performance. Moreover, the reliance on historical lease data assumes consistency in tenant behavior patterns, which may not hold true in rapidly evolving retail environments or for properties with distinct operational models such as coworking spaces or mixed-use developments. These factors should be carefully considered when adapting the model to ensure its validity and effectiveness outside the original study setting.
Sistem Pendukung Keputusan Pemilihan Penerima Bantuan Bedah Rumah Pemkab Tangerang Dengan Metode Ahp Dan Saw Mursyidin, Imam Halim; Rusdah, Rusdah
Semnas Ristek (Seminar Nasional Riset dan Inovasi Teknologi) Vol 4, No 1 (2020): SEMNAS RISTEK 2020
Publisher : Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/semnasristek.v4i1.3733

Abstract

Kabupaten Tangerang memiliki rumah tidak layak huni sebanyak 22.992 pada tahun 2018. Sayangnya, alokasi anggaran program bedah rumah masih terbatas. Sehingga perlu dilakukan penentuan prioritas dalam menentukan rumah yang akan mendapat bantuan bedah rumah. Pada Perbup Tangerang nomor 18 tahun 2017 Pasal 6 Pemkab Tangerang sudah mempunyai kriteria namun belum adanya bobot membuat Tim Teknis Dinas Perumahan, Permukiman dan Pemakaman mengalami kesulitan memilih penerima bantuan bedah rumah. Penelitian ini bertujuan untuk membantu pengambil keputusan dalam menentukan rumah mana yang menjadi prioritas mendapat program bedah rumah. Metode AHP digunakan untuk pembobotan kriteria dan metode SAW untuk tahapan perankingan. Hasil pengujian menggunakan ISO 9126 adalah untuk aspek Functionality mendapat 83,69%, aspek reliability mendapat 80,95%, aspek usability mendapat 90,75%, aspek efficiency mendapat 89,05%. Secara keseluruhan rata-rata model sistem pendukung keputusan ini direspon 86,11 % atau sangat baik.
Model Prognosis Masa Pengobatan Pasien Tuberkulosis Dengan Metode C4.5 Rusdah, Rusdah; Bregastantyo, Brian Agni
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 6: Desember 2023
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2023107393

Abstract

Pasien Tuberkulosis mempunyai jangka waktu pengobatan yang relatif beragam karena tingkat kepatuhan tiap pasien untuk meminum obat sampai dengan habis dan jangka waktu yang sudah ditentukan oleh Dokter Spesialis Paru. Apabila salah diagnosa terkait dosis obat maka akan meningkatkan faktor resiko kesehatan yaitu dimana proses pengobatan akan lebih memakan waktu dan lebih lama karena adanya kondisi Multi-Drug Resistant. Hal ini yang harus menjadi perhatian semua pihak agat tingkat kegagalan atas proses pengobatan pasien Tuberkulosis harus ditekan se minimal mungkin. Faktor  kebiasaan pasien dan waktu minum obat pasien harus dijaga ketat agar masa pengobatan dapat lebih dipersingkat. Dokter Spesialis Paru berupaya untuk menekan tingkat Drop Out pasien Tuberkulosis dengan cara mengawasi jadwal mereka dengan pengelolaan yang baik. Oleh karena itu, dibutuhkan sistem untuk membantu proses prediksi masa pengobatan pasien dengan menerapkan Cross-Industry Standard Process for Data Mining (CRISP-DM) dan menggunakan pendekatan data mining dengan mengimplementasikan algoritma C4.5 setelah dilakukan eksplorasi data menggunakan beberapa algoritma untuk klasifikasi dengan tujuan untuk hasil akurasi performa model untuk prognosis masa pengobatan pasien tuberkulosis. Melalui tahap Data Understanding dan Data Preprocessing menghasilkan atribut baru yaitu Lama Pengobatan. Dengan menggunakan 596 record mendapatkan hasil akurasi sebesar 74.33%.   Abstract Tuberculosis patients have a relatively diverse treatment period because of the level of compliance of each patient to take the drug until it runs out and the time period has been determined by the Pulmonary Specialist. If a wrong diagnosis is related to drug dosage, it will increase health risk factors, namely where the treatment process will take more time and longer due to the Multi-Drug Resistant condition. This should be the concern of all parties so that the failure rate of the treatment process for tuberculosis patients must be kept to a minimum. The patient's habit factor and the patient's time to take medication must be closely monitored so that the treatment period can be shortened. Pulmonary Specialists try to reduce the Drop Out rate of Tuberculosis patients by monitoring their schedule with good management. Therefore, a system is needed to help predict the patient's treatment period by applying the Cross-Industry Standard Process for Data Mining (CRISP-DM) and using a data mining approach by implementing the C4.5 algorithm after exploring the data using several algorithms for classification with the aim of for the results of model performance accuracy for the prognosis of the treatment period of tuberculosis patients. Through the Data Understanding and Data Preprocessing stages, a new attribute is produced, namely the Length of Treatment. By using 596 records to get an accuracy of 74.33%.
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.
Co-Authors Abdulhakim Madiyoh 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 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 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 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 Yoshua Alfia Agatha Yulianawati Yulianawati Yulianawati Yulianawati Yuliazmi, Yuliazmi Yuni Kasmawati Zaqi Kurniawan