cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Majid Rahardi
Contact Email
intechno@amikom.ac.id
Phone
+6285278711195
Journal Mail Official
intechno@amikom.ac.id
Editorial Address
Jl. Padjajaran, Ring Road Utara, Condongcatur, Depok, Sleman, Daerah Istimewa Yogyakarta 55283, Indonesia
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
INDONESIA
Intechno Journal : Information Technology Journal
Published by Universitas AMIKOM Yogyakarta
ISSN : 26551632     EISSN : 26551438     DOI : https://doi.org/10.24076/intechnojournal.2024v6i1
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering
Intechno Journal (e-ISSN 2655-1438 | p-ISSN 2655-1632) published by Universitas Amikom Yogyakarta in collaboration with Indonesian Computer, Electronics and Instrumentation Support Society (IndoCEISS) to promote high-quality Information Technology (IT) research among academics and practitioners alike, including computer scientists, Software Engineering & Big Data, Multimedia, Networking, IT professionals, and other stakeholders in the IT industry.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 77 Documents
 4   5   6   7   8 
Comparing ARIMA and Single Exponential Smoothing for Spare-Part Demand Forecasting: A Case Study at PT XYZ Angger Styo Yuniarti; Muhammad Arif Kurniawan
Intechno Journal : Information Technology Journal Vol. 8 No. 1 (2026): July
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/intechnojournal.2026v8i1.2879

Abstract

Purpose: This study aims to compare the forecasting performance of the Autoregressive Integrated Moving Average (ARIMA) and Single Exponential Smoothing (SES) methods using Battery DYC spare-part demand data from PT XYZ as an industrial case study to identify the most appropriate forecasting approach for inventory planning. Methods: Monthly sales data from January 2020 to May 2026 were analyzed using a quantitative time-series approach. The dataset was divided into training data (65 observations) and testing data (12 observations). The ARIMA model was developed according to the Box-Jenkins procedure, while the SES model used the optimal smoothing parameter estimated by SPSS. The forecast performance was assessed in terms of Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). Result/Findings: The results show that ARIMA (0,0,1) model has RMSE of 3.8370, MAE of 2.8925 and MAPE of 53.21%. SES (alpha = 0.052) has RMSE of 3.8336, MAE of 3.0000 and MAPE of 55.19%. SES is slightly better than ARIMA in terms of RMSE but ARIMA is better in MAE and MAPE, thus ARIMA is more robust overall. Novelty/Originality/Value: The study provides empirical evidence from an industrial case study that forecasting performance is dependent on demand characteristics, rather than on the universal superiority of one method over another. The findings offer practical guidance for spare-part inventory planning and provide a basis for future comparisons with specialized intermittent demand forecasting methods.
Sentiment Analysis and Emotion Detection of ChatGPT Users on Google Play Store Using the Naïve Bayes Algorithm Bagus Setya; Merlin Diana; Ikhwan Ikhwan
Intechno Journal : Information Technology Journal Vol. 8 No. 1 (2026): July
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/intechnojournal.2026v8i1.2886

Abstract

With the rapid adoption of artificial intelligence applications such as ChatGPT, understanding both users' sentiments and emotional expressions has become increasingly important for evaluating user experience and improving service quality. This study aims to analyze user sentiment and identify emotional tendencies expressed in Google Play Store reviews of the ChatGPT application. Methods: A total of 10,000 user reviews were collected through web scraping from the Google Play Store. The collected data underwent several preprocessing stages, including data cleaning, case folding, tokenization, normalization, stopword removal, and stemming. Sentiment analysis was performed using the Multinomial Naïve Bayes algorithm to classify reviews into positive, neutral, and negative categories. Emotion detection was subsequently conducted by interpreting dominant lexical patterns and frequently occurring terms within each sentiment category through Word Cloud visualization, enabling the identification of users' emotional tendencies rather than direct emotion classification. Model performance was evaluated using accuracy, precision, recall, and F1-score. Results: The sentiment distribution showed that 84.7% of the reviews were classified as positive, 3.9% as neutral, and 11.4% as negative. The best-performing model, using an 80:20 train–test split, achieved an accuracy of 86.8%, precision of 83.8%, recall of 86.8%, and an F1-score of 82.0%. The emotion detection results revealed that positive reviews were dominated by words reflecting satisfaction, usefulness, and productivity, whereas negative reviews mainly expressed frustration related to subscription costs, system errors, and application performance. Novelty: Unlike previous studies that primarily focused on sentiment classification, this research combines machine learning-based sentiment analysis with lexical interpretation of emotional tendencies derived from user-generated reviews. This integrated approach provides a more comprehensive understanding of users' perceptions and emotional responses toward ChatGPT, offering practical insights for improving AI-based application services.
Design and Feasibility Evaluation of a Web-Based Container Damage Reporting Information System for Container Terminal Operations Kencana Verawati; Mohd Faizal Sulaiman; Rully Indrawan; Euis Saribanon; Siti Maemunah; Arsad Rifki Adhawi Fareza
Intechno Journal : Information Technology Journal Vol. 8 No. 1 (2026): July
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/intechnojournal.2026v8i1.2900

Abstract

Purpose: This research designs and evaluates a web-based information system for Container Damage Report (CDR) management in container terminal operations. The study emphasizes applied computing, browser-based access, cloud data storage, and human-centered operational usability. The previous CDR process relied on paper forms, verbal confirmation, and separate photo files, resulting in slow retrieval, inconsistent documentation, and limited traceability. Methods: The research applied the 4D development model consisting of define, design, develop, and disseminate stages. The define stage identified operational problems, user roles, data needs, and reporting constraints through field observation, interviews, workflow review, and document analysis. The design stage formulated the reporting flow, required fields, database structure, interface layout, and access mechanism. The development stage implemented a no-code cloud prototype by integrating Tally for online input, Google Sheets for centralized records, and Google Sites as a web portal. The dissemination stage involved a limited introduction and user-acceptance testing. Results: The prototype supports structured online input, photo evidence upload, timestamped records, centralized cloud storage, browser-based retrieval, and monitoring of report status. User validation yielded scores of 76%, 94%, and 84%, with an average of 84.67%, indicating that the prototype is feasible for initial operational use. Novelty: The study contributes a lightweight applied computing model for container damage reporting. It shows how no-code cloud tools can support an affordable transitional architecture before integration with enterprise terminal operating systems.
Deepfake Content Analysis Using Error Level Analysis and Metadata Kurnia Nur Hikmah; Ghufron Zaida Muflih
Intechno Journal : Information Technology Journal Vol. 8 No. 1 (2026): July
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/intechnojournal.2026v8i1.2904

Abstract

Purpose: The spread of deepfakes on platform X threatens the integrity of digital information, but previous research has applied Metadata Analysis, Error Level Analysis (ELA), and Reality Defender separately, not yet as an integrated approach. This study applied Metadata Analysis and ELA to identify manipulation in deepfake images, as well as evaluate the effectiveness of Reality Defender in detecting deepfake content in images circulating on platform X as an application case study. Methods/Study design/approach: Using a descriptive qualitative approach with digital forensic experiment methods, seven purposive selected image samples representing seven content characteristic scenarios (original, face-swap, GAN, full generative AI, anti-forensics, conventionally edited, and platform compressed), analyzed through three layered stages with tiered final classification criteria based on the cross-validated Reality Defender score threshold with ELA. Result/Findings: Metadata is only informative before uploading, as X deletes EXIF uniformly post-upload. ELA remained effective in both conditions, showing localized intensity anomalies (close to 240-255 from 255) across deepfake samples. Reality Defender correctly classified six of the seven samples (85.7% accuracy, 100% in the deepfake category, 66.7% in the original category), with one false positive (64%) in the original, conventionally edited image. Novelty/Originality/Value: This study integrated all three methods simultaneously and layered with explicit criteria, showing Metadata lost post-upload diagnostic value while ELA and Reality Defender remained the most reliable for content sourced from platform X.
Flow-Based Encrypted Network Traffic Classification Using Random Forest for Network Access Control Wahyu Isnan; Yulia Fatmi; Resmi Darni; Vikri Aulia
Intechno Journal : Information Technology Journal Vol. 8 No. 1 (2026): July
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/intechnojournal.2026v8i1.2922

Abstract

Encrypted network communications reduce the effectiveness of payload based traffic identification and complicate the translation of traffic analysis into network access control decisions. This study evaluates a payload independent workflow that connects flow based multiclass classification with administrator triggered, time limited firewall enforcement. The experiment used the public ISCXVPN2016 benchmark. After removing 18,719 duplicate records, 40,987 unique flows remained; all 23 available numerical flow features were retained without feature selection or normalization. A Random Forest classifier with 300 trees was selected using five-fold cross-validated grid search on a stratified 80% training partition and evaluated on an independent 8,198 sample test set covering 14 VPN and non-VPN traffic classes. The model achieved 88.01% accuracy, 88.00% weighted precision, 88.01% weighted recall, and 87.97% weighted F1-score. It exceeded the strongest reproduced baseline, K-Nearest Neighbors, by 16.09 percentage points in accuracy and 16.29 percentage points in weighted F1-score. Supplemental five-fold evaluation produced a mean accuracy of 88.05% with a 0.36 percentage point standard deviation. The trained classifier was integrated with a web application in which administrators review predicted flows and initiate temporary MikroTik RouterOS rules. All 30 temporary blocking entries observed in the evaluation database reached the Unblocked state with recorded timestamps, demonstrating rule lifecycle traceability at the database level. The findings show that Random Forest can provide competitive flow based classification while supporting an auditable, human controlled access control workflow; however, device level reliability and cross dataset generalizability require further validation.
Mitigating COCOMO II Feature Redundancy in Software Effort Estimation via a Local Search-Enhanced Particle Swarm Optimization-SVR Framework Rahmi Rizkiana Putri; Gusti Eka Yuliastuti
Intechno Journal : Information Technology Journal Vol. 8 No. 1 (2026): July
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/intechnojournal.2026v8i1.2937

Abstract

Purpose: Software development effort estimation (SDEE) frequently suffers from accuracy degradation due to redundant project attributes in medium-sized datasets such as NASA93. Most metaheuristic optimization studies focus solely on hyperparameter tuning without filtering input dimensions, leaving the model vulnerable to overfitting. Methods/Study design/approach: This study proposes a Hybrid Local Search-Particle Swarm Optimization framework integrated with Support Vector Regression (HPSO-SVR). Instead of relying on a sequential approach, the standard PSO algorithm is modified by embedding a Simulated Annealing (SA) mechanism as a local search operator. This allows the algorithm to evaluate the feasibility of binary feature subsets and continuous parameters (C, gamma, epsilon) concurrently within a single vector space. The model is tested using a nested 10-fold cross-validation protocol on the NASA93 dataset to prevent data leakage. Result/Findings: The HPSO-SVR model successfully identified and eliminated 7 disruptive attributes (including DOCU, RUSE, and SCED), reducing the dimensionality from 22 to 15 features. This approach yielded an MMRE of 22.15%, a Pred(25) of 63.44%, and an RMSE of 38.90 PM. Statistical validation using the Wilcoxon Signed-Rank Test proved that the addition of the local search mechanism for feature selection provided a significant improvement (p < 0.05) over standard PSO-SVR. Novelty/Originality/Value: The primary contribution lies in the application of a probability-based SA local search mechanism embedded within the PSO population structure. This successfully suppresses COCOMO II feature noise simultaneously with SVR tuning, yielding a more compact and accurate predictor configuration for early-stage project planning.
Rainfall Prediction Using Random Forest with Synthetic Minority Over-Sampling Technique (SMOTE) Sri Bintang Marpaung; Ichy Lucya Resta; Tugiyo Aminoto
Intechno Journal : Information Technology Journal Vol. 8 No. 1 (2026): July
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/intechnojournal.2026v8i1.2939

Abstract

Rainfall is a crucial meteorological parameter that directly affects maritime activities and port operations, particularly in coastal regions. Tanjung Perak Port, as one of the busiest ports in Indonesia, is highly vulnerable to weather disturbances due to rainfall variability. This study develops a rainfall prediction model based on machine learning using the Random Forest algorithm optimized with the Synthetic Minority Over-Sampling Technique (SMOTE) to address data imbalance. Daily meteorological data for 2013-2024 were obtained from NASA POWER, including rainfall, minimum and maximum temperature, relative and specific humidity, wind speed, and surface pressure. Model performance was evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). SMOTE improved the model's ability to identify high-intensity rainfall events. The combined 2024-2025 evaluation produced an MAE of 1.457 mm, an RMSE of 1.819 mm, and R² of 0.861. The model therefore captures seasonal rainfall patterns and has potential as a decision-support tool for weather-risk mitigation at Tanjung Perak Port.

Page 8 of 8 | Total Record : 77

 4   5   6   7   8 

Filter by Year

2020 2026


Reset
Filter By Issues
All Issue Vol. 8 No. 1 (2026): July Vol. 7 No. 2 (2025): December Vol. 7 No. 1 (2025): July Vol. 6 No. 2 (2024): December Vol. 6 No. 1 (2024): July Vol. 5 No. 2 (2023): December Vol. 5 No. 1 (2023): July Vol. 4 No. 2 (2022): December Vol. 4 No. 1 (2022): July Vol. 3 No. 2 (2021): December Vol. 3 No. 1 (2021): July Vol. 2 No. 2 (2020): December Vol. 2 No. 1 (2020): July More Issue