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All Journal SMATIKA Sistemasi: Jurnal Sistem Informasi Jurnal Pendidikan Informatika dan Sains Sistem : Jurnal Ilmu-Ilmu Teknik Journal of System and Computer Engineering Syntax: Journal of Software Engineering, Computer Science and Information Technology Jurnal Abdimas Sang Buana Prosiding Konferensi Nasional PKM-CSR Jurnal Ilmiah IT CIDA : Diseminasi Teknologi Informasi Jurnal Ilmiah Edutic : Pendidikan dan Informatika The Journal of System Engineering and Technological Innovation Jurnal Pengabdian Masyarakat dan Inovasi Teknologi Jurnal Sistem Informasi dan Bisnis Cerdas Journal of Intelligent Software Systems Prosiding Seminar Nasional Unimus Smatika Jurnal : STIKI Informatika Jurnal
Alven Safik Ritonga
Universitas Wijaya Putra
Humanities 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 Mechanical Engineering Nursing Physics Public Health Social Sciences Other

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Public Sentiment Analysis on TikTok about Tapera Policy using Random Forest Classifier Muhandhis, Isnaini; Ritonga, Alven Safik
Sistemasi: Jurnal Sistem Informasi Vol 14, No 1 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i1.4878

Abstract

At the beginning of 2024, the Tapera policy proposed by the government sparked widespread public debate, resulting in both pros and cons. To improve the quality of public services, it is crucial for the government to evaluate policies to align with the needs and expectations of the community. This study aims to analyze public sentiment on the social media platform TikTok regarding the Tapera policy. Comment data was collected from several TikTok videos discussing the Tapera policy with high view counts. These videos received various responses in the form of comments, expressing positive, neutral, and negative sentiments about Tapera. A total of 5,036 comments were successfully scraped. The Random Forest Classifier was used for sentiment classification. This method was chosen for its ability to maintain high predictive accuracy, minimize overfitting, and perform effectively in classification tasks. The study results showed that negative sentiment dominated TikTok users' opinions, accounting for 82%, followed by neutral sentiment at 10% and positive sentiment at 8%. Many expressed disapproval for various reasons, including concerns about potential corruption, the ineffectiveness of contributions due to inflation, and the policy being burdensome amid a sluggish economy. Neutral sentiment was dominated by questions related to Tapera, such as the amount of Tapera deductions and whether participation is mandatory for those who already own a house. Positive sentiments expressed support for the Tapera policy and willingness to pay the contributions. However, the proportion of supporters of this program was significantly smaller than those opposing it. The training results of the classification model using the Random Forest Classifier achieved an accuracy of 89%. The highest F1-score for detecting negative sentiment was 94%, while the F1-score for detecting neutral sentiment was 17% and for positive sentiment, it was 32%. This disparity is due to the dataset composition being dominated by negative sentiment. The proportion of sentiment significantly influences the training of the classification model. A balanced proportion for each sentiment would enable the model to better learn and recognize the words frequently associated with each sentiment.
DEVELOPMENT OF A COURSE SCHEDULE PREPARATION APPLICATION USING GENETIC ALGORITHM Muhandhis, Isnaini; Alven Safik Ritonga; Muhammad Shubhan
Jurnal Sistem Informasi dan Bisnis Cerdas Vol. 18 No. 1 (2025): Februari 2025
Publisher : Program Studi Sistem Informasi, Fakultas Ilmu Komputer, UPN "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Preparing course schedules is an important process in campus administration. Schedule preparation must take into account existing limitations such as class limitations, teaching hours and lecturer availability. Genetic algorithm is an optimization method that can solve scheduling problems. Genetic algorithms are quite good at managing lecture schedules because they are able to solve problems with several criteria and several objectives that are modeled in the evolutionary process. This research aims to build an application to generate lecture schedules automatically with a genetic algorithm. This application is expected to help the administrative process of preparing schedules to be faster and more efficient. The research results show that the application runs well and the genetic algorithm is able to solve scheduling problems. The best genetic algorithm parameter values are population size 30, using roulette wheel selection method, mutation probability 20% and crossover probability 20% with the result of finding a solution in the 37th generation within 118 seconds
Graph-Based Fraud Detection with Optimized Features and Class Balance Azizah, Anisa Nur; Ritonga, Alven Safik; Atmojo, Suryo; Widhiyanta, Nurwahyudi; Dewi, Suzana; Murdani, M Harist; Sari, Mamik Usniyah
Journal of System and Computer Engineering Vol 6 No 3 (2025): JSCE: July 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i3.2001

Abstract

The increasing use of digital transactions also elevates the risk of fraud, particularly in credit card transactions. Fraud detection poses a challenge due to the highly imbalanced nature of the data and the complexity of relationships among entities. This study proposes a GNN-based approach, integrated with feature selection techniques and class imbalance handling through class weighting based on data distribution. Feature selection was performed using two methods: Correlation-based Feature Selection (CFS) and Random Forest Feature Importance, to obtain the most relevant features. Experimental results show that the combination of Random Forest feature selection and class weighting yielded the highest F1 Score, despite a slight decrease in accuracy. This indicates that feature selection and class weighting strategies can improve the model's ability to detect rare fraudulent transactions. This approach contributes to the development of more accurate and adaptive fraud detection systems in digital transaction environments.
Clustering Data Tweet E-Commerce Menggunakan Metode K-Means (Studi Kasus Akun Twitter Blibli Indonesia) Alven Safik Ritonga; Isnaini Muhandhis
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 12 No 01 (2022): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM UBHINUS MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v12i01.665

Abstract

The development of e-commerce is very rapid at this time, with the increasing number of e-commerce making competition in attracting customers and maintaining loyal customers. E-commerce players need to find a strategy for this, one way is advertising on social media, such as; Twitter, Facebook, Instagram, and so on. The purpose of this study was to obtain clustering of tweet data from Twitter using the K-Means method on tweet data from the Blibli Indonesia Twitter account to determine the type of tweet content that was retweeted by followers. The data used is follower tweet data which is pulled from the Twitter account @bliblidotcom. Testing the most optimum number of clusters by finding the largest Silhouette coefficient value. The results obtained that the optimal number of clusters is 10 clusters. From the results of this clustering, the tweet content that Blibli Indonesia consumers like the most is voucher content (cluster 4) and Opportunity series content (cluster 6). Voucher content and opporeno series content as a result of this clustering can be used by Blibli for promos to its consumers.
ANALISIS PERFORMA METODE YOLO DAN VIOLA-JONES PADA APLIKASI DETEKSI KANTUK Alven Safik Ritonga; Isnaini Muhandhis
Syntax : Journal of Software Engineering, Computer Science and Information Technology Vol 5, No 2 (2024): Desember 2024
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/syntax.v5i2.5404

Abstract

Pendeteksian kantuk merupakan hal penting dalam menjaga keselamatan, terutama dalam konteks transportasi dan lingkungan kerja. Dalam upaya untuk mengembangkan aplikasi pendeteksi kantuk yang efektif, metode-metode berbasis visi komputer, seperti YOLO (You Only Look Once) dan Viola-Jones, telah menjadi fokus penelitian yang signifikan. Penelitian ini bertujuan untuk membandingkan performa kedua metode tersebut dalam aplikasi pendeteksi kantuk berbasis website. Pendekatan pemecahan masalah yang terstruktur digunakan untuk mengidentifikasi kelebihan dan kekurangan dari masing-masing metode. Evaluasi dilakukan menggunakan kriteria seperti akurasi deteksi, kecepatan pemrosesan, efisiensi sumber daya, dan robustness terhadap variasi kondisi lingkungan. Data yang digunakan dalam penelitian ini meliputi dataset video dan gambar yang mencakup berbagai kondisi pencahayaan dan posisi wajah. Kedua  metode YOLO dan Viola-Jones menggunakan dataset, algoritma untuk klasifikasi menggunakan CNN (Convolutional Neural Network) dan jumlah epoch  yang sama. training metode Viola-Jones lebih cepat, 13 menit 37 detik. Dibandingkan dengan metode Yolo membutuhkan waktu 400 menit. Perbandingan keakurasian kedua metode, nilai metrik Viola-Jones 0,9 lebih besar dibandingkan metode Yolo 0,75, berarti metode Viola-Jones memiliki ketepatan yang tinggi dalam mengklasifikasi kondisi “kantuk”. Nilai metrik Recall Viola-Jones 0,88 lebih besar dibandingkan metode Yolo 0,75. Nilai metrik F1-score Viola-Jones 0,88 lebih besar dibandingkan metode Yolo 0,70, hal ini menunjukkan performa model metode Viola-Jones cukup baik dalam mendeteksi kondisi "kantuk”. Kata Kunci: Deteksi Kantuk, Metode YOLO, Metode Viola-Jones, Visi Komputer, Performa Algoritma. ABSTRACT Drowsiness detection is a crucial aspect of ensuring safety, particularly in the contexts of transportation and work environments. In the effort to develop an effective drowsiness detection application, computer vision-based methods such as YOLO (You Only Look Once) and Viola-Jones have become significant research focuses. This study aims to compare the performance of these two methods in a web-based drowsiness detection application. A structured problem-solving approach is employed to identify the strengths and weaknesses of each method. The evaluation is conducted using criteria such as detection accuracy, processing speed, resource efficiency, and robustness to environmental condition variations. The data used in this study include video and image datasets covering various lighting conditions and facial positions. Both the YOLO and Viola-Jones methods utilize the same dataset, classification algorithm (Convolutional Neural Network or CNN), and number of epochs. The training time for the Viola-Jones method is faster, taking 13 minutes and 37 seconds, compared to YOLO, which requires 400 minutes. In terms of accuracy, Viola-Jones achieves a metric value of 0.9, which is higher than YOLO's 0.75, indicating that the Viola-Jones method is more precise in classifying “drowsiness” conditions. The Recall metric for Viola-Jones is 0.88, surpassing YOLO's 0.75. Similarly, the F1-score for Viola-Jones is 0.88, greater than YOLO's 0.70. These results demonstrate that the Viola-Jones method performs well in detecting “drowsiness” conditions.Keywords: Drowsiness Detection, YOLO Method, Viola-Jones Method, Computer Vision, Algorithm Performance.
EVALUASI KINERJA YOLO V8 DAN SSD DALAM DETEKSI REAL-TIME SAMPAH BOTOL PLASTIK BERBASIS DEEP LEARNING Alven Safik Ritonga; Nurwahyudi Widhiyanta; Eka Alifia Kusnanti
Syntax : Journal of Software Engineering, Computer Science and Information Technology Vol 6, No 2 (2025): Desember 2025
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/syntax.v6i2.8020

Abstract

Sampah botol plastik merupakan salah satu fraksi paling dominan dalam timbunan sampah kota dan berkontribusi besar terhadap pencemaran lingkungan. Laporan global memperkirakan jutaan ton plastik masuk ke ekosistem perairan setiap tahun dan jumlah ini terus meningkat. Deteksi otomatis botol plastik menggunakan object detection berbasis deep learning menjadi pendekatan yang menjanjikan untuk mendukung aplikasi smart waste management seperti smart bin dan reverse vending machine. Penelitian ini mengevaluasi dan membandingkan kinerja YOLOv8 dan Single Shot MultiBox Detector (SSD) untuk deteksi real-time sampah botol plastik. Dataset yang digunakan merupakan gabungan 4.827 citra eksternal dan 251 citra internal, yang kemudian diaugmentasi menjadi lebih dari 10.000 sampel dan dianotasi untuk satu kelas bottle. Model YOLOv8 dilatih di Google Colab dengan GPU T4, sedangkan SSD diuji pada laptop berbasis CPU dalam dua skenario: (1) SSD-COCO menggunakan model pretrained umum, dan (2) SSD-Kustom yang di-fine-tune menggunakan dataset botol plastik. Hasil eksperimen menunjukkan bahwa YOLOv8 mencapai mAP@0,5 ≈ 0,984 untuk kelas botol dengan kurva precision–recall yang stabil. SSD-COCO menghasilkan sekitar 5 FPS di CPU, namun hanya mampu mendeteksi botol pada 4,07% dari 18.755 frame uji. Sebaliknya, SSD-Kustom mempertahankan FPS yang sebanding tetapi mendeteksi botol pada 100% dari 2.154 frame dengan rata-rata ≈171 deteksi per detik, yang mengindikasikan sensitivitas tinggi namun disertai gejala over-detection. Secara keseluruhan, YOLOv8 memberikan keseimbangan terbaik antara akurasi dan stabilitas, sedangkan SSD-Kustom berpotensi menjadi alternatif pada perangkat CPU-only setelah optimasi lanjutan terhadap confidence threshold dan non-maximum suppression.Kata Kunci— Sampah botol plastik, deteksi objek, YOLOv8, SSD, deep learning, real-time.ABSTRACT Plastic bottle waste is one of the most dominant fractions of municipal solid waste and contributes significantly to environmental pollution. Global reports estimate that millions of tons of plastic are discharged into aquatic ecosystems every year, with a steadily increasing trend. Automatic detection of plastic bottles using deep learning–based one-stage object detectors is a promising approach to support smart waste management applications such as smart bins and reverse vending machine. This study evaluates and compares the performance of YOLOv8 and Single Shot MultiBox Detector (SSD) for real-time plastic bottle detection. The dataset combines 4,827 external images and 251 internally acquired images, which are then augmented to more than 10,000 samples and annotated for a single bottle class. YOLOv8 is trained on Google Colab with a T4 GPU, while SSD is evaluated in two scenarios on a CPU laptop: (1) SSD-COCO using a generic pretrained model, and (2) SSD-Custom fine-tuned on the plastic bottle dataset. Experimental results show that YOLOv8 achieves mAP@0.5 ≈ 0.984 for the bottle class with high precision–recall stability. SSD-COCO reaches about 5 FPS on CPU but detects bottles in only 4.07% of 18,755 tested frames. In contrast, SSD-Custom maintains similar FPS, but detects bottles in 100% of 2,154 frames with an average of ≈171 detections per second, indicating strong sensitivity but also over-detection. Overall, YOLOv8 provides the best balance of accuracy and stability, whereas SSD-Custom becomes a viable alternative for CPU-only deployment after further optimization of confidence threshold and non-maximum suppression.Keywords— Plastic bottle waste, object detection, YOLOv8, SSD, deep learning, real-time. 
An artificial intelligence–assisted project-based hybrid learning model for enhancing students’ digital literacy competence Surya Priyambudi; Yulis Setyowati; Alven Safik Ritonga
Jurnal Pendidikan Informatika dan Sains Vol. 14 No. 2 (2025): Jurnal Pendidikan Informatika dan Sains
Publisher : Universitas PGRI Pontianak

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31571/saintek.v14i2.9420

Abstract

The urgency of this study lies in integrating three pedagogical approaches—Project-Based Learning, Hybrid Learning, and Artificial Intelligence—to enhance students’ digital literacy competence, thereby producing a learning model that can be implemented in the ICT Media course and preparing graduates with the digital literacy skills required in the era of Society 5.0. The research questions addressed in this study are as follows: (1) Is the AI-assisted Project-Based Hybrid Learning (PjBHL) model feasible for use in the ICT Media course at Universitas Wijaya Putra? (2) Is the AI-assisted PjBHL model practical to implement in the ICT Media course at Universitas Wijaya Putra? and (3) Does the use of the AI-assisted PjBHL model improve students’ digital literacy competence in the ICT Media course at Universitas Wijaya Putra? The problem-solving approach adopted in this study began with identifying students’ low levels of digital literacy competence based on classroom observations and pretest results. This condition was further analyzed by examining the limitations of the direct instruction learning model in fostering digital literacy skills, highlighting the need to explore the potential of Artificial Intelligence in supporting the PjBHL model. Accordingly, this study aims to determine the feasibility, practicality, and effectiveness of the AI-assisted PjBHL model in enhancing students’ digital literacy competence. To achieve these objectives, a development research design employing the ADDIE model—Analysis, Design, Development, Implementation, and Evaluation—was applied. Based on the five stages of the ADDIE framework, the AI-assisted PjBHL model was found to be feasible, practical, and effective in improving students’ digital literacy competence. The model also received positive responses from students and was evaluated as relevant by experts in learning, ICT, and educational technology. Overall, the average increase in students’ digital literacy competence reached 24.4 points across all indicators.
Classification of Cancer Based on RNA Data Using Elman Recurrent Neural Network Eka Alifia Kusnanti; Anisa Nur Azizah; Alven Safik Ritonga
Journal of Intelligent Software Systems Vol 5, No 1 (2026): July 2026
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v5i1.2754

Abstract

Cancer is one of the deadliest diseases whose number of sufferers continues to increaseevery year. The development of cancer cells can quickly spread to all parts of the bodythrough the bloodstream or from the lymphatic system so that it can cause death. Thiscan happen because there is a disorder that exists in the gene. The basic thing in geneticsis the monitoring of gene expression itself, namely by measuring from mRNA not fromprotein because the sequence of mRNA will hybridize with complementary DNA and RNA.The purpose of this study is to classify cancer based on RNA data using the ElmanRecurrent Neural Network method. In the recurrent network there are two inputs, namelythe actual input and the contextual input. The iteration process is much faster due tofeedback, so parameter updates and convergence are also faster. The data used are RNAdata with four classes, namely BRCA or breast adenocarcinoma (breast cancer), KIRC orkidney renal clear cell carcinoma (kidney cancer), UCEC or uterine corpus endometrialcarcinoma (uterine cancer), and LUAD or lung adenocarcinoma (lung cancer). The datawill be preprocessed using a minmax scaler then classified using ERNN with trials ofdata sharing, learning rate, and the number of hidden layers. The best combinationof parameters was obtained at 20 nodes hidden layer I, 50 nodes hidden layer II, andlearning rate 0.1. In this model, the accuracy reached 99.19 %, sensitivity of 99.03 %and specificity of 99.72 %. The time required for the model is 19 seconds.
Co-Authors Anisa Nur Azizah Anisa Nur Azizah, Anisa Nur Dwi Setyo Wibisono Eka Alifia Kusnanti Eka Alifia Kusnanti Fernando, Ferdianus Fitrah Hilmi Ghifari Harianto Putra, Ahan Budiarta Isnaini Muhandhis Mubarokah, Solton Muhammad Harist Murdani Muhammad Shubhan Muhandhis, Isnaini Ni'matul Ichsan, Muchammad Ragil Nugroho, Alfi Nurwahyudi Widhiyanta Ong Andre Wahyu Riyanto, Ong Andre Wahyu Priyambudi, Surya Purwaningsih, Endah Supeni Putra, Candra Novaliandi Pranata Rachmadian Dwi Anggara Reyhan Rizqi, Muhammad Sari, Mamik Usniyah siswadi siswadi siswadi Suharni Suharni Sujani, Sujani Suryo Atmojo, Suryo Suzana Dewi Syahputra, Adhe Rifqi Wahyu Nugroho Wasil, Mohammad Wibisono, Dwi Setyo Widhiyanta, Nurwahyudi Wijaya, Nicholas yulis setyowati