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CogITo Smart Journal
Published by Universitas Klabat
ISSN : 25412221     EISSN : 24778079     DOI : -
Core Subject : Science, Education, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering
CogITo Smart Journal adalah jurnal ilmiah di bidang Ilmu Komputer yang diterbitkan oleh Fakultas Ilmu Komputer Universitas Klabat anggota CORIS (Cooperation Research Inter University) dan IndoCEISS (Indonesian Computer Electronics and Instrumentation Support Society). CogITo Smart Journal dua kali setahun, yaitu setiap bulan Juni dan Desember. CogITo Smart Journal menerima berbagai naskah yang sifatnya baru dan asli dari hasil penelitian, telaah pustaka, dan resensi buku dari bidang Ilmu Komputer dan Informatika yang boleh ditulis dalam Bahasa Indonesia atau Bahasa Inggris. Kata CogITo berasal dari Bahasa Latin yang berarti I Think. Sehihngga CogITo Smart berarti I Think Smart dalam Bahasa Inggris.
Arjuna Subject : -
Articles 336 Documents
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Benchmarking Five Machine Learning Models for Accurate Steel Plate Defect Detection Sestri, Ellya; Karno, Adhitio Satyo Bayangkari; Hastomo, Widi
CogITo Smart Journal Vol. 11 No. 2 (2025): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v11i2.753.382-401

Abstract

Early detection of defects in steel plates is essential to ensure structural integrity and product quality in the metal manufacturing industry. This study compares the performance of five machine learning algorithms Support Vector Classifier (SVC), Nu-Support Vector Classifier (NuSVC), Decision Tree (DT), Random Forest (RF), and CatBoost (CB) to classify seven categories of steel plate defects using 26 technical features from a publicly available dataset on Kaggle. The preprocessing pipeline included outlier detection (IQR method), class imbalance correction using SMOTE, and feature normalization via StandardScaler. The models were evaluated using classification metrics such as Accuracy, Precision, Recall, F1-Score, ROC-AUC, and Log Loss. Results revealed that the CatBoost algorithm achieved the most balanced and consistent performance, with an AUC of 0.93, accuracy of 68.3%, and the lowest Log Loss value (0.786). In contrast, the Decision Tree showed severe overfitting with perfect training performance but poor generalization (Log Loss = 15.72). This study highlights the promise of CatBoost as an interpretable and efficient solution for automated defect detection in steel manufacturing, while also offering transparent reproducibility pathways for further research.
Assessing Information Security Readiness in Indonesian Fintech Companies Using KAMI Index 5.0 Framework Lestari, Merryana; Puspita, Maria Entina; Geasela, Yemima; Wijaya, Agustinus Fritz; Hiskiawan, Puguh; Vicky, Vicky
CogITo Smart Journal Vol. 11 No. 2 (2025): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v11i2.837.271-280

Abstract

The development of Indonesian financial technology (fintech) has transformed the financial industry paradigm but has also introduced significant information security risks, particularly for technology-based companies. The fintech companies should establish IT governance through an Information Security Management System (ISMS) which adheres to international standards, ensuring the confidentiality, integrity, and availability of information. This work adopts a qualitative approach deploying observations, interviews, and literature reviews on Indonesian fintech companies, especially digital banking fields, payment gateways, and digital wallet platforms. This study is to identify information security risks and assess the readiness and feasibility of implementing ISO/IEC 27001:2022 using the KAMI Index 5.0, which evaluates domains such as policy, governance, risk management, access control, incident management, asset management, and personal data protection. The research findings indicate that the electronic system of fintech companies plays a strategic role in supporting sustainability and business growth, with an implementation score of 809 and a fairly good level of information security feasibility. In conclusion, this reflects the company’s readiness for further information security implementation. The system not only supports basic operations but also serves as a key element in achieving business objectives, both internally and externally, including regulators, banking partners, and customers.
Comparative Analysis Of Convolutional Neural Network Models For Digital Image-Based Melanoma Classification Kurniawati, Ana; Haura, Aniqoh Hana
CogITo Smart Journal Vol. 11 No. 2 (2025): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v11i2.856.257-270

Abstract

Melanoma is one of the most malignant forms of skin cancer, with an incidence rate of 7.9% in Indonesia. Traditional biopsy-based diagnosis, though crucial, is invasive and time-consuming, creating barriers for early detection. To address this issue, this research compares two Convolutional Neural Network (CNN) models for digital image-based melanoma classification. The study utilized a publicly available dataset from Kaggle, consisting of 17,805 images (melanoma and non-melanoma), which were divided into training, validation, and testing subsets. The models were trained using the Adamax and SGD optimizers for 100 epochs. The performance of the models was evaluated based on accuracy, loss, precision, recall, and F1-score. The CNN model with the best architecture, which consisted of two fully connected layers, achieved an accuracy of 93.18% and a loss of 0.1636, outperforming the alternative model. These results confirm the effectiveness of CNN models in classifying melanoma images and support the development of a web-based platform that allows users to upload or capture images for rapid and non-invasive detection.
Smart Assistive Stick with Arduino and Multidirectional Ultrasonic Sensors for Intelligent Obstacle Detection and Navigation Sinaga, Mikha; Ibrahim, Ayub; Sembiring, Nita
CogITo Smart Journal Vol. 11 No. 2 (2025): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v11i2.886.311-322

Abstract

Blindness or visual impairment restricts spatial awareness and increases the risk of collisions, falls, and mobility challenges. This study presents the design and development of a Smart Assistive Stick with Arduino and multidirectional ultrasonic sensors for intelligent obstacle detection and navigation. Unlike conventional white canes that provide only short-range tactile feedback, the proposed system employs multidirectional sensing to detect obstacles from various directions within a range of 0.1 to 4 meters. Intelligent feedback is delivered through both haptic and auditory signals, with an average response delay of only 200 ms, ensuring timely and reliable navigation assistance. Testing showed detection accuracy exceeding 85%, continuous battery life of 6–8 hours, and a total device weight of 600 grams, making it lightweight and suitable for daily use. While performance decreases in noisy environments due to ultrasonic interference, the system demonstrates novelty in extending detection range, incorporating multidirectional sensing, and providing intelligent real-time feedback. These contributions establish the smart assistive stick as a more effective and user-friendly mobility aid compared to traditional solutions. 
Digitization and Virtualization of Minahasan Bamboo Instruments: Development of a Culturally-Informed Virtual Studio Technology Plugin Najoan, Xaverius; Neman, Meily Ivane Esther; Kembuan, Roger Allan Christian
CogITo Smart Journal Vol. 11 No. 2 (2025): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v11i2.895.478-491

Abstract

Traditional musical instruments are an important component of intangible cultural heritage, yet many remain underrepresented in contemporary digital music production. In particular, Minahasan bamboo instruments face limited accessibility due to the lack of digital instrument representations. This study addresses this issue by developing a culturally informed Virtual Studio Technology (VST3) plugin that digitizes and simulates the characteristic sounds of Minahasan bamboo music. The proposed approach combines field recording, digital audio processing, and sample-based virtual instrument development using the JUCE framework. The resulting plugin was evaluated through functional, compatibility, performance, and cultural fidelity testing across multiple digital audio workstations. Experimental results demonstrate accurate MIDI-to-audio translation, low-latency performance, stable operation, and efficient CPU usage. User evaluations further confirm the authenticity of the sound and the usability of the interface. The findings indicate that VST-based digitization offers a practical and transferable solution for preserving traditional musical instruments while enabling their integration into modern music production workflows.
Predictive Linear Regression Model for Premature Birth Risk Assessment System Kusumaningsih, Dewi; Kadir, Abdul; Pudoli, Ahmad
CogITo Smart Journal Vol. 11 No. 2 (2025): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v11i2.924.402-413

Abstract

Preterm birth is a major cause of neonatal mortality in Indonesia and is influenced by multiple maternal factors. Early prediction models are crucial for supporting timely clinical decision-making and reducing adverse maternal–infant outcomes. The method of this study developed a linear regression–based predictive model using 915 pregnancy medical records from Dr. H. M. Ansari Saleh Regional Hospital, Banjarmasin (2020–2022). The workflow included data preprocessing, feature selection, Min-Max normalization, and experimentation with various train–test split ratios (90:10 to 50:50). Model performance was evaluated using R², Adjusted R², MAE, MSE, RMSE, and MAPE metrics. As the results, the 70:30 split ratio achieved the best accuracy of 89.05% and AUC of 98.10%, with low prediction errors. Optimizations with Adamax and Nadam enhanced stability and reduced MAPE to 1.95%. The optimized linear regression model reliably predicts preterm birth risk and is suitable for clinical decision support, particularly in resource-limited settings.
Forecasting Medium Rice’s Retail Price with Machine Learning in Gorontalo Province Giu, Jamal Darusalam; Gaib, Amalan Fadil; Rasyid, Abdul
CogITo Smart Journal Vol. 11 No. 2 (2025): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v11i2.930.462-477

Abstract

The stability of rice prices is essential for food security in Indonesia, particularly in Gorontalo Province where volatility has increased in recent years. This study develops a machine learning-based forecasting framework using Decision Tree, Random Forest, and K-Nearest Neighbors (KNN) to estimate next-day retail prices. A harvest-season indicator was incorporated to capture agricultural seasonal patterns. Data preprocessing included feature engineering, data cleaning, exploratory analysis, and chronological splitting to maintain temporal order. Model performance was assessed using RMSE and MAPE. The optimized KNN model achieved the highest accuracy, with an RMSE of 96.76 and a MAPE of 0.4%, demonstrating its strength in capturing short-term price fluctuations. The integration of seasonal indicators further improved predictive performance compared to univariate approaches, offering practical value for supporting timely policy interventions. This study is limited by its narrow feature set and the absence of external drivers such as weather conditions, production shocks, and distribution disruptions. Future research may incorporate additional exogenous variables or explore deep learning and hybrid ensemble methods to enhance robustness and generalizability.
Design and Implementation of Full-Stack Conference System for Streamlined Administrative Workflows Pakpahan, Andrew Fernando
CogITo Smart Journal Vol. 11 No. 2 (2025): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v11i2.934.368-381

Abstract

This study presents the design, implementation, and evaluation of the 11ISC Conference Management System (CMS), a full-stack web application developed to address the fragmented administrative workflows of the 11th International Scholars Conference. Using the Design Science Research methodology, the system was created in response to recurring challenges such as manual registration, accommodation and transportation coordination, and the time-intensive preparation of Letters of Acceptance. The CMS was evaluated through blackbox functional testing covering twelve primary use cases, all of which passed successfully, including participant registration, payment verification, automated LoA generation, QR-based check-in, and accommodation assignment. Administrator feedback indicated substantial process improvements, with the automated LoA module reducing preparation time by up to 90 percent and integrated room and check-in management significantly decreasing errors associated with the previous spreadsheet-based workflow. Deployed during the conference, the system supported more than 220 participants and over 180 paper submissions, providing real-time dashboards and unified data management. The results demonstrate that the CMS enhances efficiency, accuracy, and coordination, offering a practical and replicable solution for academic event management in similar institutional contexts.
Hyperparameter Tuning Exploration to Maximize MobileNet Performance in Classification Kidney Tumor Siregar, Sandy Putra; Safii, M.; Andani, Sundari Retno
CogITo Smart Journal Vol. 11 No. 2 (2025): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v11i2.960.447-461

Abstract

The main focus in this research is how to develop the MobileNet architecture in order to produce a kidney tumor classification model that is accurate, resistant to overfitting, and remains consistent with variations in datasets and training parameters. This study aims to develop MobileNet architecture to produce a software model that can precisely identify with high accuracy, perform kidney tumor classification, and avoid failure in generalizing new data called overfitting, as well as evaluate the difference in accuracy generated from several variations of datasets and parameters. The method used in this study is MobileNet with hyperparameter tuning and fine-tuning, and it was compared with the MobileNet Baseline method. The dataset consists of 12,446 images classified as Normal, Cyst, Stone, and Tumor, collected from Kaggle. The findings of this study on the division of the 80:10:10 ratio of the proposed method image data resulted in 100% accuracy, 100% precision, 100% recall, and 100% F1-Score. This study is expected to produce architecture modifications that can classify kidney tumors with high accuracy so that the hypothesis is achieved. In addition, various approaches in medical image analysis using deep learning have shown better results in identifying various tumors, especially this research in the classification and detection of kidney tumors.
Forecasting the Highest and Lowest Prices in Financial Markets Using a VMD-LSTM Hybrid Model Purwantara, I Made Adi; Kusrini, Kusrini; Setyanto, Arief; Utami, Ema
CogITo Smart Journal Vol. 11 No. 2 (2025): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v11i2.963.295-310

Abstract

Accurate forecasting of the lowest and highest prices in financial markets poses a considerable challenge due to the inherent nonlinear behaviour, non-stationarity, and high noise levels of financial time series data. Most prior studies focus only on closing prices, with limited attention to the simultaneous prediction of high and low prices. Yet, predicting the lowest and highest prices is essential for investors to make informed trading decisions. To address this gap, this study proposes a hybrid DL framework that integrates VMD and LSTM networks for predicting daily high and low prices simultaneously. This study used 12 years of daily data from three diverse assets: AUD/USD, TLKM, and XAU/USD. The data underwent preprocessing, VMD-based decomposition, and were input into the LSTM. The dataset was split 80% for training and 20% for testing. Experiments varied the number of decomposition modes (K = 7, 10, 12) and sliding window sizes (5, 15, 30, 45, 60, 90). Results show that the VMD-LSTM model exhibits improved performance in most of the tested scenarios compared to traditional LSTM. These findings underscore that the use of VMD decomposition can help enhance the accuracy of forecasting the highest and lowest prices in the financial market.

Page 33 of 34 | Total Record : 336

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