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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 356 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
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
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
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
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
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.
PERBANDINGAN KINERJA HYBRID INSET–SVM DAN SENTISTRENGTH_ID–SVM UNTUK ANALISIS SENTIMEN PLATFORM TELEMEDICINE HALODOC DAN ALODOKTER Asy Syifaur Roisah Rufaida
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 9 No 2 (2026): Jurnal SKANIKA Juli 2026
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v9i2.3705

Abstract

The quality of telemedicine platforms can be assessed through sentiment analysis of user opinions on social media. This study compared two hybrid methods, InSet-SVM and SentiStrength_id-SVM, for sentiment analysis of the Halodoc and Alodokter platforms. Both lexicons were selected because they apply similar sentiment-weighting mechanisms but differ in vocabulary coverage. Data were collected from X (formerly Twitter) using TWINT with the keywords “Halodoc” and “Alodokter” from January 1 to December 31, 2022. The data were labeled using each sentiment lexicon, while TF-IDF was applied for feature weighting before classification using a Support Vector Machine (SVM). The models were evaluated using three-times-repeated 10-fold cross-validation. The results showed that InSet-SVM outperformed SentiStrength_id-SVM across all evaluation metrics for both datasets. For Halodoc, InSet-SVM achieved 85.92% precision, 86.24% recall, an F1-score of 85.76%, and 86.24% accuracy. For Alodokter, it achieved 83.86% precision, 84.28% recall, an F1-score of 83.59%, and 84.28% accuracy. These findings indicate that using InSet as the initial labeling lexicon produces a more consistent SVM model across both datasets.
SISTEM MONITORING PAKAN HEWAN PELIHARAAN BERBASIS INTERNET OF THINGS MEMANFAATKAN LIMBAH BOTOL PLASTIK Muhammad Febrian Rachmadhan Amri; Komang Arya Widyanthara; I Nyoman Angga Prabawa; Made Prastha Nugraha
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 9 No 2 (2026): Jurnal SKANIKA Juli 2026
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v9i2.3724

Abstract

The primary challenge motivating this research is pet owners' difficulty in consistently feeding pets while away from home. This study aims to design an Internet of Things (IoT)-based Smart Pet Feeder (SPF) that emphasizes real-time food level monitoring, utilizing repurposed plastic bottles as food containers. The system employs a Raspberry Pi 3 Model B, an HC-SR04 ultrasonic sensor to detect food levels, and an MG995 servo motor to dispense food. Developed in Python, three automated main engines synchronize data with a MySQL database. Validation involved Black-box testing and quantitative evaluations of sensor accuracy and feed output consistency. Results show the system passed Black-box testing with a 100% functional success rate. The ultrasonic sensor achieved 95.83% accuracy (4.17% average error). Food output averaged a consistent 25 grams per servo cycle. Using a 1.5-liter plastic bottle proved effective, economical, and eco-friendly. In conclusion, the system successfully enables accurate remote monitoring and feeding via the internet.
VISUALISASI DAN ANALISIS HISTOGRAM WARNA PADA CITRA PENYAKIT BSR UNTUK MENDUKUNG TAHAP PRA-PEMPROSESAN CITRA Beri Perima; Chairani Chairani
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 9 No 2 (2026): Jurnal SKANIKA Juli 2026
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v9i2.3781

Abstract

Basal Stem Rot (BSR) is an important disease affecting oil palm plants that can reduce productivity. Image-based analysis can support disease detection, but color distribution characteristics need to be understood before the classification stage. This study aims to analyze the color distribution of oil palm leaf images from Healthy and BSR classes using histograms in the RGB and HSV color spaces and to evaluate the discriminative ability of color features. The dataset consisted of 2,438 images obtained from the open Roboflow repository. Each image was processed through 224×224-pixel resizing, Gaussian Blur, RGB-to-HSV conversion, and extraction of 12 statistical parameters consisting of mean and standard deviation. Differences between classes were tested using the Mann–Whitney U test, while the magnitude of differences was measured using Cohen’s d. The discriminative ability of G_Mean, S_Mean, and V_Mean was evaluated using the Receiver Operating Characteristic (ROC) and Area Under the Curve (AUC). The results showed significant differences among the three features (p-value = 0.0000) with large effect sizes. The AUC values for G_Mean, S_Mean, and V_Mean were 0.9794, 0.7362, and 0.9790, respectively. These findings indicate that color distribution analysis can identify discriminative features to support feature selection and BSR image preprocessing design.
KLASIFIKASI KELAYAKAN KREDIT MENGGUNAKAN RANDOM FOREST DENGAN OPTIMISASI SMOTENC DAN GRIDSEARCHCV Nandito Diaz Vannesa; Fikri Budiman
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 9 No 2 (2026): Jurnal SKANIKA Juli 2026
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v9i2.3829

Abstract

Creditworthiness is critical to financial system stability, yet conventional methods struggle with imbalanced data and mixed numeric-categorical features. This study develops the CreditRF-OptSHAP pipeline, integrating Random Forest, SMOTENC, GridSearchCV, and SHAP to improve credit classification performance and interpretability. The dataset used, Statlog German Credit Data (UCI), comprises 1,000 instances, 20 mixed features, and a 70:30 imbalance ratio. The pipeline comprises five phases: data exploration; preprocessing via label encoding and StandardScaler; SMOTENC-based training-set balancing; hyperparameter optimization via GridSearchCV with Stratified 10-Fold Cross Validation; and model interpretation via SHAP TreeExplainer so that each feature's contribution to prediction is explained, making the model no longer a black box. The significance of this recall gain over RF Default was validated using McNemar's Test to rule out statistical coincidence. The main model (RF+SMOTENC+GridSearchCV) achieved a recall of 0.5833 for the bad credit class and an AUC-ROC of 0.7638, up from 0.5000 for RF Default. SHAP analysis identified checking account status as the most dominant feature (importance 0.1239). The McNemar test confirmed a statistically significant difference (p=0.041), confirming its validity. The CreditRF-OptSHAP pipeline yields a model that is more sensitive to non-performing loans, transparent, and statistically validated, thus supporting accountable credit decisions in financial institutions.
PREDIKSI TREN PUBLIKASI BLOCKCHAIN DAN KRIPTOGRAFI DI INDONESIA MENGGUNAKAN BIBLIOMETRIK DAN LSTM Fathimah Azzahro Hasni Rifayanti; Rahmat Budiarsa
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 9 No 2 (2026): Jurnal SKANIKA Juli 2026
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v9i2.3843

Abstract

The rapid adoption of digital technologies in Indonesia has driven increasing research on Blockchain and Cryptography, yet comprehensive mapping and projections of their development remain limited. This study aims to analyze the intellectual landscape and predict publication trends in these two technologies in Indonesia using a quantitative approach integrating bibliometric analysis and Machine Learning. Data were obtained from Dimensions.ai for the 2020–2024 period, yielding 2,193 relevant documents after data cleansing. Network analysis using VOSviewer reveals a shift in Blockchain research from financial assets toward practical applications, particularly Supply Chain management and system efficiency. Meanwhile, Cryptography research shows polarization between the development of classical algorithms and applications for IoT security. A Long Short-Term Memory (LSTM) model was then developed to predict publication trends for 2025–2029 based on annual data from 2020–2024. The model achieved a Mean Absolute Percentage Error (MAPE) below 5% on the test data; however, the limited number of data points should be considered when interpreting this accuracy. LSTM projects a flattening publication trend through 2029, with Cryptography expected to surpass Blockchain by approximately 55 documents. These findings indicate that security and trust may become increasingly critical priorities compared with Blockchain infrastructure development.

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