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INDONESIA
SKANIKA: Sistem Komputer dan Teknik Informatika
Published by Universitas Budi Luhur Jakarta
ISSN : -     EISSN : 27214788     DOI : 10.36080
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Engineering
SKANIKA: Sistem Komputer dan Teknik Informatika adalah media publikasi online hasil penelitian yang diterbitkan oleh Program Studi Sistem komputer dan Teknik Informatika, Fakultas Teknologi Informasi, Universitas Budi Luhur. Scope atau Topik Jurnal: Kriptografi, Steganografi, Sistem Pakar / Artificial Intelligence , Sistem Penunjang Keputusan, Bioinformatika, Kecerdasan Komputasional, Semantics Web dan Ontologies, Data Mining,Text Mining,Natural Language Processing, Pengelolaan Citra Digital, Otomasi Berbasis Sensor, Wireless Sensor Network, Network Management dan Maintenance, Sistem Operasi, Sosial Network Analysis, Security, Augmented Reality, Game Development, Virtual Reality, Webservice / API, Internet of Things (IoT)
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 345 Documents
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RANCANG BANGUN EARLY WARNING SYSTEM BANJIR TERINTEGRASI MONITORING CUACA BERBASIS IOT DAN LOGIKA FUZZY Alfa Hardinata Wibowo; Halim Agung; Makmun ZA; Nunung Nurmaesah
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 9 No 2 (2026): Jurnal SKANIKA Juli 2026 (In Press)
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v9i2.3704

Abstract

Sistem peringatan dini banjir berbasis Internet of Things (IoT) dikembangkan sebagai upaya mitigasi risiko banjir yang terjadi secara tiba-tiba akibat tingginya curah hujan. Penelitian ini bertujuan merancang dan menguji sistem monitoring lingkungan yang mampu memberikan informasi kondisi cuaca dan risiko banjir secara real-time. Sistem menggunakan sensor ultrasonik untuk mengukur ketinggian air, sensor YF-S201 untuk kecepatan aliran air, sensor DHT22 untuk suhu dan kelembapan, sensor hujan untuk intensitas curah hujan, serta sensor LDR untuk kondisi cahaya, dengan ESP32 sebagai pengendali utama. Data sensor diproses menggunakan metode Fuzzy Logic Mamdani untuk menentukan tingkat risiko banjir dan mendukung pengambilan keputusan secara real-time. Hasil perhitungan selanjutnya dikirimkan kepada pengguna melalui notifikasi otomatis pada aplikasi Telegram. Hasil pengujian menunjukkan bahwa sistem mampu mengklasifikasikan status peringatan banjir ke dalam kategori Aman, Waspada, dan Bahaya dengan tingkat akurasi rata-rata sebesar 97,52%. Dengan demikian, sistem ini berpotensi menjadi solusi pendukung mitigasi banjir yang sederhana, efektif, dan mudah diterapkan di lingkungan masyarakat.
ANALISIS SENTIMEN KEBIJAKAN PEMERINTAHAN PRABOWO DI PLATFORM X MENGGUNAKAN NAIVE BAYES DAN SVM Muhammad Faris Kurniawan; Anik Hanifatul Azizah
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 9 No 2 (2026): Jurnal SKANIKA Juli 2026 (In Press)
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v9i2.3726

Abstract

Social media has become a major platform for the public to express opinions regarding political issues and government policies. This study aims to analyze Indonesian public sentiment toward the government policies of President Prabowo Subianto on Platform X and compare the performance of the Naïve Bayes and Support Vector Machine (SVM) algorithms for sentiment classification. Data were collected through a web crawling process using relevant keywords and subsequently underwent several preprocessing stages, including text cleaning, case folding, slang normalization, tokenization, stopword removal, and stemming. Sentiment labeling was performed automatically using a lexicon-based approach with a specially compiled Indonesian sentiment dictionary, with weighting referring to the VADER method. Text features were extracted using the Term Frequency–Inverse Document Frequency (TF-IDF) method, and the dataset was divided into training and testing sets using an 80:20 ratio. Model performance was evaluated using accuracy, precision, recall, and F1-score. The labeling results show a sentiment distribution of 42.3% positive, 33.7% negative, and 24.1% neutral. The experimental results indicate that the SVM algorithm outperformed Naïve Bayes in classifying public sentiment toward government policies, achieving an accuracy of 89% compared to 70% for Naïve Bayes. Furthermore, the specially compiled lexicon-based labeling approach proved effective in producing a large-scale training dataset without requiring manual annotation.
RANCANG BANGUN SISTEM PENGENDALI SUHU BARREL PADA MESIN SINGLE SCREW EXTRUDER MENGGUNAKAN METODE PID BERBASIS ESP32 Yani Prabowo; Anwar Rifai; Jan Everhard
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 9 No 2 (2026): Jurnal SKANIKA Juli 2026 (In Press)
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v9i2.3773

Abstract

Barrel temperature stability is a crucial factor in the polymer extrusion process, as temperature fluctuations directly affect melt viscosity and final product quality. This study designed a temperature control system for a single-screw extruder using an ESP32 microcontroller to compare the performance of the Proportional-Integral-Derivative (PID) method against a system without control (open loop). The research methodology involved testing various screw rotation speeds from 50 to 200 RPM to observe barrel temperature stability and melt homogeneity. The determination of the PID parameters in this study was based on the evaluation of the open-loop system's thermal response mapping, yielding optimal tuning values of Kp = 4.8, Ki = 0.09, and Kd = 55. The test results showed that the system without control experienced temperature fluctuations of ±8–12°C, while the PID controller successfully maintained temperature stability with a low deviation of ±1–3°C. Furthermore, the PID system was able to automatically compensate for temperature increases caused by material friction at high speeds. The implementation of PID control was proven to increase thermal stability by up to 80% compared to conventional systems. These results demonstrate that the use of PID algorithms on microcontrollers ensures a more precise and stable plastic extrusion process compared to traditional methods.
OPTIMASI MOBILENETV2 DENGAN PRUNING DAN QUANTIZATION UNTUK DETEKSI PENYAKIT DAUN PADI PADA PERANGKAT EDGE Nur Dwi Priyambodo; Edi Sugiarto
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 9 No 2 (2026): Jurnal SKANIKA Juli 2026 (In Press)
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v9i2.3828

Abstract

Rice leaf diseases Bacterial Leaf Blight (BLB), Brown Spot, and Leaf Blast pose a serious threat to food security, with potential yield losses of 30–70%. Existing deep learning models are typically large (>80 MB), making them impractical for farmers' low-specification smartphones. This study proposes MobileNetV2 optimization through 40% magnitude-based weight pruning and Post-Training Quantization (PTQ) Int8 using TensorFlow Lite for Android deployment. The dataset is sourced from a Mendeley repository (DOI: 10.17632/fwcj7stb8r/1) comprising 5,932 images across 4 classes, from which a 3-class subset (BLB, Brown Spot, Leaf Blast) totaling 4,804 images was used, split 70-15-15 into training, validation, and test sets. Evaluation was performed on 721 held-out test images, excluded from the training, validation, and quantization calibration processes. The proposed model achieves 2.89 MB (88.23% reduction from the 24.58 MB baseline), an inference speed of 14.53 ms/image on an Intel CPU workstation, and an accuracy of 98.34% (macro F1-Score 0.9831). More aggressive 50% pruning caused accuracy to degrade to 83.50% post-quantization, confirming 40% as the best trade-off among the sparsity levels tested. Grad-CAM analysis validated the biological relevance of the extracted features for each disease class. Future work includes on-device validation on Android hardware and expanded disease class coverage
KOMPARASI MNB, CNB, DAN SVM UNTUK DETEKSI UJARAN KEBENCIAN BAHASA INDONESIA Fisco Maulana Ikhwan; Edi Sugiarto
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 9 No 2 (2026): Jurnal SKANIKA Juli 2026 (In Press)
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v9i2.3830

Abstract

Indonesia had 235.26 million internet users and 81.72% internet penetration in 2026, while hate speech threatens social cohesion. Based on the literature reviewed in this study, no prior study has been found that systematically conducted a three-way benchmark of MNB, CNB, and SVM with preprocessing ablation, McNemar testing, and Wilson CI error analysis in Indonesian hate speech detection, using a dataset of 13,169 tweets, with per-algorithm imbalanced-class handling (SMOTE for MNB/CNB; CSL and Lexicon-Based Coefficient Override for SVM), and ablation of six preprocessing configurations. MNB achieves F1 Macro 72.10% and highest Abusive Recall (84.30%), while SVM achieves Abusive Recall 80.20% with inference latency 0.10 ms on CPU; a direct comparison with IndoBERT on GPU T4 is not fully equivalent across hardware, though an additional CPU-only measurement shows IndoBERT at ~102.4 ms (~1,020× ratio). McNemar tests confirm SVM differs significantly from MNB/CNB (p<0.0001), CNB vs MNB non-significant (p=0.0704), indicating a negative SMOTE×CNB interaction specific to this study’s dataset and configuration. SVM is selected as the deployment model based on Utility Score (81.39) under the weighting scenarios tested, and may serve as a first-pass screening aid (not a substitute for human verification) for platforms operating under Indonesian ITE Law No. 1/2024.

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