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Program Studi Ilmu Komputer Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Halu Oleo Kampus Hijau Bumi Tridharma Jalan H. E. A. Mokodompit, Anduonohu Kendari, Sulawesi Tenggara - Indonesia 93232
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AnoaTIK: Jurnal Teknologi Informasi dan Komputer
Published by Universitas Halu Oleo
ISSN : -     EISSN : 29877652     DOI : https://doi.org/10.33772/anoatik
Core Subject : Science,
Computer Science & IT
AnoaTIK: Jurnal Teknologi Informasi dan Komputer (eISSN 2987-7652) merupakan salah satu jurnal yang dikelola oleh program studi Ilmu Komputer pada Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Halu Oleo. Terbit 2 (dua) kali dalam setahun pada bulan Juni dan Desember sebagai salah satu wadah publikasi ilmiah pada bidang teknologi informasi dan ilmu komputer berbahasa Indonesia.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 9 Documents
 1 
Search results for , issue "Vol 3 No 2 (2025): Desember 2025" : 9 Documents clear
ANALISIS FAKTOR KUALITAS PELAYANAN UPA PERPUSTAKAAN UNIVERSITAS HALU OLEO DENGAN METODE SEM-LISREL Mohammad Ricky Ramadhan Rasyid
AnoaTIK: Jurnal Teknologi Informasi dan Komputer Vol 3 No 2 (2025): Desember 2025
Publisher : Program Studi Ilmu Komputer FMIPA-UHO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33772/anoatik.v3i2.114

Abstract

This study aims to analyze the factors influencing service quality at the Library of Halu Oleo University by employing the Structural Equation Modeling (SEM) method supported by LISREL 8.80 software. The variables examined include service quality (X1) and library facilities (X2) as exogenous variables, as well as perceived quality (Y) as the endogenous variable. Data were collected through questionnaires distributed to 100 library users from various faculties and academic years who visited the library. Each indicator was measured using a five-point Likert scale. The data were analyzed through several stages, including tests of normality, validity, reliability, measurement model estimation, and structural model evaluation. The results of the validity and reliability tests showed that the indicators used met the required criteria for loading factor, t-value, Construct Reliability (CR), and Variance Extracted (VE). The structural model analysis revealed that service quality had a positive and significant effect on perceived quality, with a coefficient of 0.78. Meanwhile, library facilities also had a positive and significant effect, with a coefficient of 0.29. The R² value of 0.89 indicates that 89% of the variance in perceived quality can be explained by these two variables. This study demonstrates that service quality plays the most dominant role in enhancing users’ perceptions of library service quality, followed by library facilities. These findings emphasize the importance of improving service performance and managing library facilities effectively to create an optimal service experience for users
IMPLEMENTASI MODEL LONG SHORT-TERM MEMORY PADA PREDIKSI HARGA SAHAM BERBASIS WEB Fadhillah Muslimin; Andi Tenriawaru; Muhammad Riansyah Tohamba
AnoaTIK: Jurnal Teknologi Informasi dan Komputer Vol 3 No 2 (2025): Desember 2025
Publisher : Program Studi Ilmu Komputer FMIPA-UHO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33772/anoatik.v3i2.116

Abstract

This study aims to develop a web-based stock price prediction system using the Long Short-Term Memory (LSTM) algorithm to forecast the highest and lowest prices of stocks listed in the LQ45 index. LSTM was chosen for its ability to recognize long-term patterns in time series data and its more stable performance compared to methods such as ARIMA and GRU. The system features an interactive interface and user activity logging to enhance usability and user experience. Evaluation results show that the LSTM model performs well, with MAPE below 3% and RMSE values varying according to stock volatility. The best results were achieved by ACES, with RMSE values of 28,772 (High) and 27,142 (Low), and MAPE of 2,19% and 2,2%, while AMMN had the highest error rates with RMSE values of 247,154 and 281,926, and MAPE of 2,42% and 2,79%. The system successfully delivers real-time predictions through a responsive and user-friendly web interface.
KLASIFIKASI STATUS GIZI BAYI MENGGUNAKAN METODE K-NEAREST NEIGHBOR Sri Ayu Lestari; Natalis Ransi; Muhammad Arfan
AnoaTIK: Jurnal Teknologi Informasi dan Komputer Vol 3 No 2 (2025): Desember 2025
Publisher : Program Studi Ilmu Komputer FMIPA-UHO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33772/anoatik.v3i2.117

Abstract

The development of a baby nutritional status classification system was carried out by applying the K-Nearest Neighbor (KNN) method with the aim of supporting more accurate monitoring of infant growth and development. The system determines the nutritional status of infants based on input data including age, weight, height, and mid-upper arm circumference, which are then compared with available training data. The system development process employed the waterfall approach, encompassing requirements analysis, system design, implementation, and testing stages. Testing was conducted using black box testing to ensure that all system functions operated according to requirements, as well as a confusion matrix to measure classification accuracy. The results showed that all system features functioned properly and the achieved accuracy rate reached 97.30%, indicating that the system has very good performance in effectively supporting the monitoring of infant nutritional status.
DESAIN DAN IMPLEMENTASI APLIKASI SIMULASI TOEFL MENGGUNAKAN ALGORITA RANDOM NUMBER GENERATOR BERBASIS WEB Muh Istikmal Husain; Gunawan; Budi Wijaya Rauf
AnoaTIK: Jurnal Teknologi Informasi dan Komputer Vol 3 No 2 (2025): Desember 2025
Publisher : Program Studi Ilmu Komputer FMIPA-UHO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33772/anoatik.v3i2.120

Abstract

This research aims to develop a web-based TOEFL simulation application system using the RNG algorithm to randomize questions. The study was conducted using three different scenarios for different test types (listening, reading, and structure). The first scenario involved 10 trials for each test type to test the consistency and randomness of the algorithm used. The second test scenario was conducted to assess the quality of randomization when there was a large amount of data in the question bank stored in the database. The third test scenario was conducted to examine the results of randomization when there were two users with the same access time but different user IDs. The randomization process was carried out through a previously designed web application and used the MCG equation, one of the methods in the RNG algorithm. The results of the research conducted indicate that the use of the RNG algorithm using the MCG method can be applied well to the TOEFL simulation system. For example, the results of the first scenario test showed that in each trial, the resulting question order was different. For example, in the structure test, a question with a certain number may appear in position 18 on the first trial, but in subsequent trials it may shift to position 27 or 32; The test results also found that even though both users had the same access time, differences in User ID values caused different initial seed values. This small difference in seed values resulted in a significantly different question order.
SISTEM PAKAR DIAGNOSA PENYAKIT HEWAN TERNAK SAPI MENGGUNAKAN METODE FORWARD CHAINING Milda Wati; Laode Saidi; Ferdinand Murni Hamundu
AnoaTIK: Jurnal Teknologi Informasi dan Komputer Vol 3 No 2 (2025): Desember 2025
Publisher : Program Studi Ilmu Komputer FMIPA-UHO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33772/anoatik.v3i2.125

Abstract

Sistem pakar bertujuan agar komputer mampu memberikan solusi cerdas seperti manusia. Salah satu algoritma yang digunakan adalah forward chaining. Berdasarkan wawancara dengan dokter hewan, jumlah dokter hewan di Kota Kendari masih sangat kurang, sehingga diagnosa penyakit ternak terkendala. Penelitian ini bertujuan menghasilkan sistem pakar untuk mendiagnosa penyakit sapi menggunakan metode Forward Chaining. Metode forward chaining merupakan metode runut maju, yang berarti menggunakan kondisi aksi. Metode ini akan menggunakan data dalam menentukan aturan mana yang akan dijalankan, setelahnya aturan tersebut akan dijalankan. Forward chaining dapat disebut sebagai metode intervensi yang dimana melakukan penalaran pada suatu masalah kepada solusinya. Metode ini sendiri menggunakan pendekatan goal-driven, diawali dengan hipotesis yang dilanjutkan dengan mencari bukti pendukung dari hipotesis tersebut Hasil uji blackbox menunjukkan seluruh fungsi sistem berjalan baik. Uji confusion matrix menunjukkan akurasi 87.5%, presisi 100%, dan recall 87.5%. Nilai akurasi dari yang kurang begitu baik dari sistem disebabkan oleh faktor keterbatasan intervensi yang dilakukan oleh sistem. Meskipun begitu sistem telah mampu digunakan secara luas dan diharapkan dapat membantu dalam proses diagnosa penyakit pada hewan ternak sapi.
ANALISIS KOMPARASI KESTABILAN ALGORITMA DECISION TREE DAN RANDOM FOREST UNTUK KLASIFIKASI GANGGUAN TIDUR Almunajat Amirul Soleh; Gusti Arviana Rahman
AnoaTIK: Jurnal Teknologi Informasi dan Komputer Vol 3 No 2 (2025): Desember 2025
Publisher : Program Studi Ilmu Komputer FMIPA-UHO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33772/anoatik.v3i2.126

Abstract

Sleep disorders are serious health conditions that are closely associated with an increased risk of hypertension and cardiovascular disease. Considering the high cost of conventional clinical diagnostic procedures, data mining techniques for early detection offer an effective alternative. This study aims to conduct a comparative performance analysis between the Decision Tree (C4.5) and Random Forest algorithms in classifying sleep disorders (None, Insomnia, and Sleep Apnea). To address class imbalance issues, this research utilizes the secondary Sleep Health and Lifestyle dataset. To evaluate model stability, a rigorous validation method, Stratified 10-Fold Cross-Validation, is employed. The experimental results indicate that the Random Forest algorithm outperforms the Decision Tree, achieving an average accuracy of 91.41% and a Kappa value of 0.8473. The primary advantage of the Random Forest algorithm lies in its ability to significantly improve the detection sensitivity of the Insomnia class to 88.31%. Based on feature importance analysis, diastolic blood pressure and the BMI category (Overweight) are identified as the most influential features in the diagnostic process. Random Forest is therefore considered a more accurate and stable model for medical decision support systems.
ANALISIS DAN IMPLEMENTASI SISTEM KONTROL LAMPU RUMAH OTOMATIS MENGGUNAKAN METODE FUZZY LOGIC I Made Alit Dwi Saputra; Ilham Julian Efendi; Subardin
AnoaTIK: Jurnal Teknologi Informasi dan Komputer Vol 3 No 2 (2025): Desember 2025
Publisher : Program Studi Ilmu Komputer FMIPA-UHO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33772/anoatik.v3i2.128

Abstract

Efficient use of electrical energy is one of the important aspects in a smart home system. One solution is an automatic lighting control system that can turn lights on or off based on environmental conditions. This study aims to design and implement an automatic home lighting control system using the Mamdani fuzzy logic inference method, with the integration of the Internet of Things (IoT) through the Blynk application for remote monitoring and control. This system usesanLDR sensor to detect environmental lighting levels and a PIR sensor to detect human movement. Data from the sensors is processed using predetermined rule-based fuzzy logic to produce lighting control decisions (ON or OFF). The implementation offuzzy logic is carried out on an Arduino microcontroller, while IoT connectivity and user interface are handled by the ESP8266 module connected to the Blynk platform. The test results show that the system can work automatically with responsiveness and accuracy. The lights turn on only when the light is dim/dark and there is movement, and can be controlled manually through the Blynk application. This system also allows monitoring ofsensor values (LDR and PIR) in real time via mobile devices. With the integration offuzzy logic and IoT, the system is proven to be effective in improving energy efficiency and user comfort in the home environment
PENGARUH PENGGUNAAN GITHUB DAN EDUTRACK TERHADAP MOTIVASI MAHASISWA DALAM PROYEK PERANGKAT LUNAK Muhammad Riansyah Tohamba; LM Bahtiar Aksara
AnoaTIK: Jurnal Teknologi Informasi dan Komputer Vol 3 No 2 (2025): Desember 2025
Publisher : Program Studi Ilmu Komputer FMIPA-UHO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33772/anoatik.v3i2.129

Abstract

The integration of digital technologies in education continues to advance, including the use of GitHub as a collaboration platform and EduTrack as a progress monitoring tool. This study aims to evaluate the impact of GitHub and EduTrack on students’ motivation during software project development. A total of 104 students participated in this study, utilizing GitHub for team project management and EduTrack for periodic progress evaluation. Data were collected through a Likert-scale questionnaire designed to measure autonomy, competence, relatedness, and industry readiness. Statistical analysis using the t-test revealed that several dimensions of students’ motivation were significantly influenced by the use of these platforms. The findings provide new insights into the integration of digital tools to support collaborative learning and prepare students for industry challenges.
PERBANDINGAN METODE CERTAINTY FACTOR DAN DEMPSTER SHAFER DALAM MENDIAGNOSIS PENYAKIT DIABETES MELLITUS PADA RUMAH SAKIT UMUM DAERAH KOTA KENDARI Wasda Bil Husna; Herdi Budiman; La Surimi
AnoaTIK: Jurnal Teknologi Informasi dan Komputer Vol 3 No 2 (2025): Desember 2025
Publisher : Program Studi Ilmu Komputer FMIPA-UHO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33772/anoatik.v3i2.130

Abstract

Diabetes Mellitus is a chronic disease that requires early and accurate diagnosis. This study aims to develop an expert system to diagnose Type 2 Diabetes Mellitus by comparing Certainty Factor and Dempster-Shafer methods. The system was developed using rule-based reasoning with symptom data and expert confidence values. Evaluation using a confusion matrix shows that the Certainty Factor method achieved higher accuracy than the Dempster-Shafer method.

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