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Aiti: Jurnal Teknologi Informasi
Published by Universitas Kristen Satya Wacana
ISSN : 16938348     EISSN : 26157128     DOI : https://doi.org/10.24246/aiti
Core Subject : Science,
Computer Science & IT
AITI: Jurnal Teknologi Informasi is a peer-review journal focusing on information system and technology issues. AITI invites academics and researchers who do original research in information system and technology, including but not limited to: Cryptography Networking Internet of Things Big Data Data Science Software Engineering Information System Web Programming Mobile Application Service System Artificial Intelligence Digital Image Processing Machine Learning Deep Learning Geographic Information System Context Aware System Management Information System Software-defined Network
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 149 Documents
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Komparasi performa model machine learning algoritma XGBoost dan Random Forest pada studi kasus mendeteksi stunting Hadisuwarno, Muhamad Anggito Herlambang; Martono, Kurniawan Teguh; Adriono, Erwin
AITI Vol 22 No 2 (2025)
Publisher : Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/aiti.v22i2.266-278

Abstract

Stunting is a chronic malnutrition problem caused by long-term inadequate nutritional intake, which causes children to be shorter than the standard height for their age. Stunting is often considered a hereditary factor; thus, this condition can lead to people becoming passive without proper preventive measures. Early detection is crucial for effective intervention. This study compares the XGBoost and Random Forest algorithms for detecting stunting in children and addresses the complex challenges associated with this process. Data were obtained from Kaggle and the Semarang City Health Office. The research went through a pre-processing stage before being combined. Optuna was used for automated hyperparameter tuning to achieve optimal accuracy. The results demonstrated the success of the stunting detection model, achieving an accuracy of 85.26% for XGBoost and 85.78% for Random Forest using unbalanced data, and 88.42% for XGBoost and 85.78% for Random Forest using balanced data. This study demonstrates these algorithms can address malnutrition issues effectively.
Perbandingan Naïve Bayes dan KNN dalam menganalisis sentimen pengguna terhadap UI/UX pada aplikasi IKD Anumi, Maria Grassella; Manongga, Danny
AITI Vol 22 No 2 (2025)
Publisher : Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/aiti.v22i2.165-177

Abstract

The Directorate General of Citizens and Civil Registration, under the Ministry of Home Affairs, has developed an application called digitization of population documents (IKD) to enhance administrative services related to community registration and increase user satisfaction. However, this application has its strengths and weaknesses, which have resulted in mixed reactions from users. To analyze user sentiment towards the application's user interface and user experience, the research utilized Naïve Bayes and KNN techniques. The study involved 52 respondents, and the results showed that the Naïve Bayes algorithm has a higher accuracy rate than the KNN algorithm. Sentiment predictions for the user interface obtained 38 positive responses, 12 neutral responses, and two negative responses, while for user experience, the study obtained 41 positive responses, eight neutral responses, and three negative responses. The accuracy rates of the Naïve Bayes algorithm for the user interface and user experience were 94.23% and 90.38%, respectively. On the other hand, the KNN algorithm achieves an accuracy rate of 71.15% for the user interface and 88.46% for the user experience. Overall, the study shows that the Naïve Bayes method outperforms the KNN method in terms of accuracy for user interface and user experience.
Penerapan algoritma Long Short-Term Memory untuk prediksi jalan berlubang Maulana, Daffa; Hidayati, Nurtriana
AITI Vol 22 No 2 (2025)
Publisher : Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/aiti.v22i2.178-191

Abstract

Dinas Pekerjaan Umum Bina Marga dan Cipta Karya Provinsi Jawa Tengah merupakan instansi pemerintah yang bertanggung jawab terhadap perencanaan, pembangunan jalan serta prasarana umum lainnya. Persoalan muncul ketika menentukan kota mana yang harus mendapat perhatian khusus dalam pemeliharaan jalan, dengan mempertimbangkan kondisi jalan yang memerlukan perawatan lebih ekstensif. Penelitian ini bertujuan untuk mengatasi masalah tersebut dengan memanfaatkan metode Long Short-Term Memory (LSTM) untuk memprediksi jalan berlubang. Penelitian ini memanfaatkan data yang diperoleh dari rekaman jalan berlubang yang dikumpulkan dari 9 Balai Pengelolaan Jalan (BPJ) di Provinsi Jawa Tengah, mulai tanggal 1 Januari 2023 hingga 31 Desember 2023. Temuan penelitian menunjukkan bahwa Long Short-Term Memory (LSTM) dapat mengantisipasi jalan berlubang secara akurat pada 9 dataset BPJ yang ditunjukkan dengan nilai Mean Absolute Percentage Error (MAPE). Berdasarkan hasil evaluasi, terlihat bahwa performa model berbeda-beda di berbagai lokasi. Model dengan tingkat akurasi tertinggi terdapat pada BPJ Purwodadi dengan Mean Absolute Percentage Error (MAPE) sebesar 1,932868 persen. Model ini dilatih menggunakan variasi batch 48 dan 200 epoch. Sebaliknya model dengan tingkat akurasi terendah terdapat pada BPJ Wonosobo dengan Mean Absolute Percentage Error (MAPE) sebesar 30,511073 persen. Model ini dilatih menggunakan variasi batch 48 dan 50 epoch.
Exploring organizational readiness for ERP HRM implementation using the McKinsey 7S Framework Cahayadi, Putri Andy; Faza, Ahmad; Wiratama, Jansen
AITI Vol 22 No 2 (2025)
Publisher : Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/aiti.v22i2.236-250

Abstract

This study explores the readiness of PT Halim Lestari Mandiri (PT HLM) for implementing an ERP HRM system, addressing challenges such as high costs and lengthy deployment times. Using the McKinsey 7S framework, a qualitative approach involving focus group discussions (FGDs) was employed to understand factors affecting organizational performance and effectiveness. Four HRM department employees, selected for their knowledge of ERP and understanding of business processes, contributed to data collection through moderated FGDs. Thematic analysis was employed to analyze the data. Results show that a successful ERP HRM implementation requires a comprehensive strategy, covering organizational structure, strategic planning, shared values, technological integration, leadership, communication, and skill development. PT HLM's readiness and adaptability, along with strategic employee development and shared values, create a strong foundation for successful implementation. Improved technological systems and inclusive leadership support accurate decision-making and efficient operations, while ongoing training ensures employees remain well-prepared. This research underscores the importance of an integrated, multifaceted approach to achieving ERP HRM success. It recommends policymakers develop policies that foster organizational readiness and ongoing skill development to adapt to technological advances. The study adds to existing knowledge by providing a detailed, module-specific readiness assessment and emphasizes the need for flexible structures and continuous training to ensure successful ERP HRM implementation.
Deteksi kendaraan di lalu lintas menggunakan Kalman Filter dan Yolo v8 Pratama, Leonnyndra Putra; Pakereng, Magdalena A. Ineke
AITI Vol 22 No 2 (2025)
Publisher : Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/aiti.v22i2.192-205

Abstract

This research discusses the development of a vehicle detection and tracking model for traffic using the YOLO v8 algorithm integrated with a Kalman Filter. The goal of this study is to improve vehicle tracking accuracy in traffic video recordings, with enhanced robustness against occlusions and disturbances. The methodology involves data collection from a vehicle image dataset available on Roboflow, followed by data processing into training, validation, and testing subsets. The model was trained over 30 epochs using Google Colaboratory, achieving a Mean Average Precision (mAP50) of 93%, a precision of 90%, and a recall of 89%. Testing was conducted on traffic footage from the City of Madiun, obtained from the Madiun City Government's CCTV website, demonstrating high detection and tracking performance. Model evaluation results indicate an accuracy of 93%, precision of 96%, recall of 85%, and an F1 score of 90%. The confusion matrix evaluation shows good performance in detecting vehicles, including cars, motorcycles, and trucks, making it a potentially effective solution for traffic monitoring challenges.
Implementasi sistem monitoring kualitas air berbasis IoT pada penampung mata air di daerah Larier Ambon Kainama, Marchel Devid; Purnomo, Hindriyanto Dwi
AITI Vol 22 No 2 (2025)
Publisher : Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/aiti.v22i2.206-220

Abstract

Penelitian ini mengembangkan sistem monitoring kualitas air berbasis Internet of Things (IoT) di Larier, Kota Ambon. Sistem menggunakan mikrokontroler Arduino ESP32 dan platform Blynk untuk memantau parameter suhu, pH, dan Total Dissolved Solids (TDS) secara real-time. Sensor-sensor dikalibrasi untuk memastikan akurasi, dan evaluasi kinerja menggunakan Mean Absolute Percentage Error (MAPE) menunjukkan hasil baik, khususnya pada sensor pH dan TDS. Pengujian lapangan dilakukan pada kondisi cuaca hujan dan panas untuk menilai stabilitas pengukuran. Hasil menunjukkan nilai TDS lebih tinggi pada kondisi panas, pH lebih tinggi saat hujan, sedangkan suhu relatif stabil. Sistem ini memungkinkan masyarakat memantau kualitas air secara mandiri melalui aplikasi, serta mendukung pengelolaan sumber daya air yang berkelanjutan. Implementasi ini menunjukkan potensi teknologi IoT dalam meningkatkan kesadaran dan partisipasi masyarakat terhadap kualitas lingkungan lokal.
Analisis sentimen masyarakat terhadap PON 2024 Aceh dan Sumatra Utara di Twitter menggunakan algoritma SVM dan KNN Sari, Fajri Diannita; Widayat, Widi
AITI Vol 22 No 2 (2025)
Publisher : Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/aiti.v22i2.251-265

Abstract

Pekan Olahraga Nasional (PON) 2024 yang diselenggarakan di Aceh dan Sumatra Utara menjadi salah satu peristiwa olahraga terbesar yang menarik opini publik melalui media sosial, khususnya Twitter. Penelitian ini membandingkan performa algoritma Support Vector Machine (SVM) dan K-Nearest Neighbor (KNN) dalam analisis sentimen masyarakat terhadap PON 2024. Data yang dikumpulkan melalui crawling menghasilkan 1.458 tweet, yang terdiri dari 1.321 tweet berlabel positif dan 137 tweet berlabel negatif. Data tersebut kemudian diproses menggunakan tahapan pre-processing teks. Hasil penelitian menunjukkan bahwa SVM dengan TF-IDF menghasilkan akurasi tertinggi, yaitu 95,45% pada data training dan 91,78% pada data testing, dengan F1-score 96%, yang menunjukkan performa paling stabil. Sementara itu, KNN menunjukkan hasil terbaik pada K = 4 dengan Word Embedding, dengan akurasi 95,20% pada data training dan 92,12% pada data testing, namun memiliki variasi kinerja yang lebih besar dibandingkan SVM. Penelitian ini menunjukkan bahwa algoritma SVM dan KNN dapat diandalkan untuk analisis sentimen media sosial, dengan kontribusi signifikan terhadap evaluasi persepsi publik terkait PON 2024.
Evaluasi empiris model ARIMA dan LSTM dalam konteks peramalan penjualan mobil Toyota Caesar, Yulius Bagus; Hadiono, Kristophorus; Ardhianto, Eka
AITI Vol 22 No 2 (2025)
Publisher : Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/aiti.v22i2.221-235

Abstract

The increasing population and economic growth in major Indonesian cities drive demand for motor vehicles, particularly those of the Toyota brand. However, the fluctuating sales of Toyota from 2011 to 2023 complicate sales planning. This study aims to compare the performance of ARIMA and LSTM forecasting models in predicting Toyota car sales in Indonesia. The data used were obtained from the Association of Indonesian Automotive Industries (GAIKINDO). The results show that the LSTM model performs better than the ARIMA model in forecasting sales. The LSTM model yields lower prediction error values, as indicated by an RMSE of 5,198.40 and a MAPE of 15%, compared to ARIMA, which has an RMSE of 7,769.82 and a MAPE of 16%. Although the MAE of ARIMA is slightly better, at 4501.91, LSTM can minimize large prediction errors, which is evidenced by the significantly lower RMSE compared to ARIMA. The 1% difference in MAPE indicates that LSTM has a smaller percentage of prediction errors. These findings provide important implications for automotive companies in formulating more effective sales strategies. The LSTM model can be a valuable tool for anticipating market trends and making more accurate business decisions.
Klasifikasi sentimen pada ulasan pengguna aplikasi Cryptocurrency di Google Play Store menggunakan algoritma Decision Tree Tsuroyya, Kamiliya; Umam, Khothibulu; Yuniarti, Wenty Dwi; Handayani, Maya Rini
AITI Vol 22 No 2 (2025)
Publisher : Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/aiti.v22i2.279-293

Abstract

Cryptocurrency has become a trend in digital investment. The Pintu application exemplifies the use of digital technology for trading cryptocurrency assets. Reviews from the Google Play Store serve as an important source of data to understand the opinions of Pintu application users. This study focuses on investigating the sentiment analysis of Pintu application users sourced from the Google Play Store by implementing the Decision Tree and Random Forest algorithms. The approach used involves collecting data from the Google Play Store, which contains user reviews and ratings. The data is then labeled as positive or negative and cleaned, processed, and analyzed using Decision Tree and Random Forest algorithms. The results of the study showed that the accuracy of the Decision Tree reached 0.90, while the Random Forest achieved an accuracy of 0.88. From these results, it can be concluded that the Decision Tree is superior in classifying text mining with high accuracy. The difference between the two methods is insignificant in terms of accuracy, specifically for Decision Tree, with an accuracy of 0.90, Precision of 0.91, and recall of 0.95, and Random Forest, with an accuracy of 0.88, precision of 0.87, and recall of 0.95. User sentiment analysis of the Pintu application provides a positive response to using the Pintu application.

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