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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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ANALISIS SENTIMEN PADA MEDIA SOSIAL TERHADAP LAYANAN SAMSAT DIGITAL NASIONAL DENGAN SUPPORT VECTOR MACHINE Kirana, Anindya Sasi; Rusdah, Rusdah; Roeswidiah, Ririt; Pudoli, Ahmad
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.
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.
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%.
XgBoost Hyper-Parameter Tuning Using Particle Swarm Optimization for Stock Price Forecasting Pebrianti, Dwi; Kurniawan, Haris; Bayuaji, Luhur; 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.
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