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All Journal SMATIKA Sistemasi: Jurnal Sistem Informasi 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 Prosiding Seminar Nasional Unimus Smatika Jurnal : STIKI Informatika Jurnal
Alven Safik Ritonga
Universitas Wijaya Putra
Humanities 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 Public Health Social Sciences Other

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PENGELOLAAN AIR BESIH "TIRTA AGUNG"DESA WRINGIN KURUNG BARU KAB. GRESIK Nugroho, Alfi; Sujani, Sujani; Ritonga, Alven Safik
Prosiding Konferensi Nasional Pengabdian Kepada Masyarakat dan Corporate Social Responsibility (PKM-CSR) Vol 7 (2024): PKMCSR2024: Kolaborasi Hexahelix dalam Optimalisasi Potensi Pariwisata di Indonesia: A
Publisher : Asosiasi Sinergi Pengabdi dan Pemberdaya Indonesia (ASPPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37695/pkmcsr.v7i0.2488

Abstract

Dusun Wringin Kurung merupakan salah satu dusun Kabupaten Gresik, pada tahun 2016 wilayah tersebut di lakukan pengeboran sumur sedalam 100m sekaligus pembangunan tandon 12 kubik atas bantuan dari pemerintah Pada saat ini HIPPAM pengelola Air bersih Tirta Agung sudah disalurkan kepada 271 rumah warga. Manajemen HIPPAM masih sangat sederhana, struktur organisasi HIPPAM hanya terdiri dari Ketua, wakil teua, Sekretaris, Bendahara, teknisi dan operasional. Metode mengacu pada tujuan kegiatan yang telah ditetapkan yaitu dari masalah produksi air dari sumur langsung dipompa ke tandon kemudian dialirkan ke warga, sehingga seringkali air disertai pasir dan terkadang keruh Belum memliki cadangan pompa dan pipa untuk calon sumur baru karena letak sumber yang berjauhan dengan pengadaaan dan instalasi pompa baru, perbaikan dan pendampingan instalasi pipa tersier dan penambahan filter plug n play di beberapa saluran tersier. Internalisasi budaya kerja yang baik di lingkungan pengurus denagn mengadakan pelatihan dan pendampingan budaya organisasi, penataan SDM, rekrutment pegawai baru dan pelatihan pengelolan usaha, pelatihan perhitungan harga pokok penjualan air bersih termasuk musayawarah dengan warga dan pengurus Desa dan BPD. Hasil kegiatan ini adalah produksi air lebih bersih dan dalam pengelolaan manajemen lebih baik dengan adanya aplikasi yang di kembangkan.
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.
EVALUASI KINERJA YOLO V8 DAN SSD DALAM DETEKSI REAL-TIME SAMPAH BOTOL PLASTIK BERBASIS DEEP LEARNING Ritonga, Alven Safik; Widhiyanta, Nurwahyudi; Kusnanti, Eka Alifia
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. 
Co-Authors Anisa Nur Azizah, Anisa Nur Dwi Setyo Wibisono Fernando, Ferdianus Fitrah Hilmi Ghifari Harianto Putra, Ahan Budiarta Isnaini Muhandhis Kusnanti, Eka Alifia Mubarokah, Solton Muhammad Harist Murdani Muhammad Shubhan Muhandhis, Isnaini Ni'matul Ichsan, Muchammad Ragil Nugroho, Alfi Ong Andre Wahyu Riyanto, Ong Andre Wahyu 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