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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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Prediksi kelulusan tepat waktu mahasiswa untuk pemantauan program studi menggunakan metode data mining Rachardian, Seprima; Sediyono, Eko
AITI Vol 21 No 2 (2024)
Publisher : Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/aiti.v21i2.168-182

Abstract

Penelitian ini melakukan eksplorasi data (data mining) menggunakan data mahasiswa pada Program Studi sarjana (S1) di Universitas PQR tahun akademik 2023/2024. Penelitian bertujuan memprediksi kelulusan tepat waktu mahasiswa sesuai dengan syarat pemantauan Badan Akreditasi (masa studi tepat waktu mahasiswa adalah empat tahun). Parameter data pengujian menggunakan data master mahasiswa, data transaksi mahasiswa, dan data status kelulusan mahasiswa angkatan 2019 pada tahun akademik 2023/2024. Pengujian dilakukan menggunakan metode algoritma k-Nearest Neighbors (k-NN). Hasil data training diperoleh accuracy 75%, nilai precision 75%, dan nilai recall 0%. Data testing algoritma k-NN memperoleh hasil accuracy 87.76%, nilai precision 89.19%, dan nilai recall 83.33%. Hasil uji data training dan data testing menunjukkan persentase yang cukup tinggi untuk tidak lolos pemantauan. Pimpinan Perguruan Tinggi dapat mengambil langkah awal dari hasil prediksi tersebut, guna mengambil kebijakan akademik untuk meningkatkan lulusan tepat waktu.
Using the Support Vector Machine method with the HOG feature for classification of orchid types Andayani, Sri; Kusneti, Leni
AITI Vol 21 No 1 (2024)
Publisher : Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/aiti.v21i1.82-95

Abstract

Orchids are the most species-rich flowering plants, with approximately 750 genera and 43,000 types of orchids in the world, of which about 5,000 species have been recorded in several provinces in Indonesia. Orchids have beautiful flowers with attractive colors, making them ornamental plants that many people like. From plant morphology, orchid plants can be differentiated based on the morphology of flowers, leaves, fruit, stems, and roots. The leaves of orchid plants have their characteristics for each type of orchid, such as long, round, or lanceolate. All orchids have veins that run parallel to their leaves. The individual shapes of orchid leaves can be classified using a Support Vector Machine (SVM) and Histogram of Gradient (HOG). In this research, five types of orchids that are popular among orchid lovers were used, namely Dendrobium, Cattleya, Oncidium, Phalaenopsis, and Vanda orchids, which were taken from public data. The accuracy of this method in classifying orchid species based on leaf morphology can be measured using a confusion matrix that measures precision, recall, and accuracy. From five tests, the Oncidium orchid had the highest average accuracy with a value of 98%, the Vanda orchid had the highest average precision of 99.80%, and the Cattleya orchid had the highest average recall of 100%.
Sistem deteksi pada transformator menggunakan Dissolved Gas Analysis (DGA) dengan metode Logistic Regression Wibowo, Kurniawan Indra; Hendry, Hendry
AITI Vol 21 No 2 (2024)
Publisher : Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/aiti.v21i2.197-209

Abstract

Penelitian ini membahas implementasi sistem deteksi kegagalan pada transformator menggunakan  analisis gas terlarut Dissolved Gas Analysis (DGA) dengan menerapkan metode Logistic Regression. Tujuan penelitian ini adalah membuat sistem yang dapat meningkatkan keandalan transformator, mengurangi risiko gangguan listrik, dan memberikan kontribusi pada praktik industri berupa penerapan metode yang efisien dalam pemeliharaan transformator daya. Penggunaan data DGA dari transformator yang mengalami berbagai tingkat kerusakan sebagai sampel pelatihan dan pengujian menunjukkan bahwa Logistic Regression memberikan performa yang sangat baik dengan tingkat akurasi, presisi, F1-Score, dan recall berturut-turut sebesar 97%, 97%, 97%, dan 97%. Hasil ini mencerminkan kemampuan metode ini dalam mengklasifikasi hasil positif dan negatif. Sementara itu key gas memiliki akurasi, presisi, dan F1-Score yang rendah (42%, 17%, dan 24%), Recall 42% yang sedikit lebih tinggi menunjukkan kemampuan metode ini untuk mendeteksi sejumlah besar positif aktual, tetapi dengan tingkat kesalahan yang lebih tinggi. Penemuan ini berpotensi meningkatkan keandalan sistem tenaga listrik melalui pemeliharaan dini dan deteksi tepat waktu terhadap potensi masalah pada transformator.
A comparative study on classification models for stock rating prediction Yap, Justin; Wiradinata, Trianggoro
AITI Vol 21 No 1 (2024)
Publisher : Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/aiti.v21i1.140-151

Abstract

The digital transformation in the stockbroker industry has led to a significant increase in retail investors, who often lack the expertise to analyse stocks thoroughly. This research addresses the challenge by proposing a classification model to predict stock ratings such as "Reduce", "Hold", "Moderate Buy", and "Buy”, allowing retail investors to make informed decisions. The data analysed is collected from the S&P 500 index through web scraping using Beautiful Soup, resulting in a dataset used for training and testing the classification model. Popular stock indicators are used as attributes in predicting the rating of the stock, which includes the Exchange, Price, Volume, Market Cap, ROE, ROA, P/E Ratio, EPS, Annual Sales, Net Income, Net Margins, and PB Ratio of the stock. The models selected for classification include K-Nearest Neighbors (k-NN), Gaussian Naive Bayes, Support Vector Machine (SVM), Decision Tree, and Random Forest. GridSearch is employed to maximize each algorithm's parameters for optimal performance. Results indicate that the k-NN model outperforms others, achieving the highest accuracy (0.618644) and weighted F1-score (0.605011). However, all models exhibit relatively low accuracy, suggesting the complexity of predicting stock ratings due to external factors not considered in the study.
Kematangan risiko keamanan informasi layanan TI menggunakan pendekatan NIST dan standar ISO 27001:2013 (Studi kasus: Bapenda Provinsi Jawa Tengah) Aminudin, Agus; Supriyanto, Aji
AITI Vol 21 No 2 (2024)
Publisher : Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/aiti.v21i2.210-229

Abstract

The application of Information Technology (IT) often poses risks, such as incorrect application processes, data theft and data corruption. With the increasing risk, greater control is needed. For this reason, it is necessary to see whether the running system is equipped with adequate control. The Regional Revenue Management Agency (BAPENDA) of Central Java Province has utilized IT in its activities. The absence of adequate information security standards impacts data or information that is less secure, both in terms of confidentiality, integrity, and availability. The aims and objectives of the research are to measure KAMI risk maturity, such as conducting an IT assessment managed by BAPENDA. For example, vehicle tax payment service application, Android (New Sakpole), and IT infrastructure. The results of KAMI Maturity Level at BAPENDA in security policy clauses were 0.76, organization KAMI 1.24, control asset classification 0.63, personnel security 1.12, incident management KAMI 1.21, business continuity management 0.51, physical and environmental security 1.61, system development and maintenance 2.94, access control 4.18, communications and operations management 4.58 and, compliance 2.07. Mapping asset identification with NIST-CSF obtained several assets: hardware, software, employee, and information/data. The results show that assets in BAPENDA have a high risk (High) Risk Avoidance, so they require mitigation using NIST controls and Annex ISO-IEC 27001:2013.
Analisis konten budaya kolaboratif berbasis Grounded Theory menggunakan Text Mining Julians, Adhe Ronny; Manongga, Daniel Herman Fredy; Hendry, Hendry
AITI Vol 21 No 2 (2024)
Publisher : Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/aiti.v21i2.230-250

Abstract

Creating a collaborative culture of innovation in an organization is very important today. A collaborative culture of innovation is not just about physically working together but also about creating an environment that supports open communication, appreciation for new ideas, and acceptance of risk. Organizations that embrace this culture can create significant added value and thrive in an ever-changing environment. This research aims to conduct a content analysis of several Grounded Theory-based reputable scientific articles using Text Mining, which involves using coding techniques to classify information and identify certain categories or codes representing certain text elements. The analysis results are a conceptual network model that connects elements that influence collaborative culture on innovation, such as Openness, Diversity, Shared Goals, Trust, Teamwork, Support, and Use of Technology. Organizations use this model to create a collaborative culture of innovation in their environment, and it can be used in further research to test the model using statistical tests.
Rancang bangun sistem pemantauan dan penyiraman pintar tanaman cabai pada greenhouse menggunakan Fuzzy Mamdani berbasis Blynk IoT Arief, Zainal; Zarory, Hilman; Jufrizel , Jufrizel; Mursyitah, Dian
AITI Vol 21 No 2 (2024)
Publisher : Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/aiti.v21i2.271-284

Abstract

One of the leading horticultural commodities cultivated by farmers is chili. However, chili farmers often experience problems monitoring soil moisture levels and environmental air temperature. In this case, it can affect the growth of chili plants, which impacts crop failure. Therefore, to overcome these problems, this research develops Mamdani fuzzy logic to build an intelligent system that can automatically monitor and water chili plants. In this research, the impact of the use of sensors and system impact treatment on chili plants will be tested and compared with plants that do not use the system. The results of this study show that the smart monitoring and watering system can function properly and maximize the condition of the plant media compared to those that do not use the system. The system can control soil moisture levels with an average error of 3.73% and air temperature with an average error of 1.03%. The results of the system by comparing plants that do not use the system combined with greenhouses can protect plants from extreme weather such as heat, rain, pests, and lack of wild plant growth so that plants look lush, lush, and grow quickly to minimize farmer crop failure. It is easier for farmers to monitor using the Blynk IoT application.
Implementasi analisis sentimen pada ulasan aplikasi Duolingo di Google Playstore menggunakan algoritma Naïve Bayes Apriliyani, Meli; Musyaffaq, Mirza Izzal; Nur’Aini, Siti; Handayani, Maya Rini; Umam, Khotibul
AITI Vol 21 No 2 (2024)
Publisher : Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/aiti.v21i2.298-311

Abstract

Penelitian ini menyelidiki analisis sentimen evaluasi Aplikasi Duolingo menggunakan metode Naive Bayes. Program Duolingo mencontohkan penggunaan teknologi data besar untuk pemrosesan data yang luas dan rumit. Google Play Store menawarkan fungsi peninjauan dan pemeringkatan yang dapat membantu pengembangan program dan perbaikan aspek yang tidak diinginkan.  Proyek ini menggunakan teknik analisis sentimen yang secara otomatis menganalisis ulasan produk internet Indonesia dan mendapatkan informasi mengenai perasaan yang diungkapkan dalam ulasan tersebut. Metode Naïve Bayes digunakan untuk menentukan klasifikasi ulasan menjadi positif atau negatif. Temuan penelitian menunjukkan bahwa kumpulan data yang terdiri dari 1000 data yang berasal dari ulasan program Duolingo di Google Play Store diberi label secara manual sebelum ke langkah prapemrosesan. Dari jumlah tersebut, 500 data memiliki sentimen positif, sedangkan 500 data memiliki sikap negatif. Selain itu, analisis sentimen menunjukkan tingkat akurasi sebesar 86%. Skor f1 menunjukkan nilai presisi 89% dan recall 83%, dengan hasil f1 pada klasifikasi sebesar 86%.
Evaluasi kualitas website e-learning UNIPA menggunakan metode Webqual 4.0 Yang, Ester Deborah; Baisa, Lorna Yertas; Sanglise, Marlinda
AITI Vol 21 No 2 (2024)
Publisher : Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/aiti.v21i2.285-297

Abstract

Computer and internet technology advancement has prompted higher education institutions to utilize online services such as e-learning in teaching and learning activities. This research aims to measure the quality of the University of Papua (UNIPA) e-learning website from the user's perspective by employing the WebQual 4.0 approach, which encompasses the dimensions of usability, information quality, service interaction, design appearance, and user satisfaction. This study is expected to obtain specific insights to enhance the effectiveness and convenience of website usage, supporting UNIPA's progress in digital education. Based on 200 student respondents, it was found that UNIPA's e-learning website excels in information quality with an average score of 4.2 and significantly influences user satisfaction. Ease of use and design appearance are also strengths, with scores of 3.9 and 3.7, respectively, and have a significant impact. However, service interaction still needs improvement, with a score of 3.4, although it has a significant influence. Overall, user satisfaction is reasonably good, with a score of 3.8, but improving service interaction needs to be addressed by UNIPA's management. These findings illustrate user needs and serve as a basis for development to enhance the efficiency and effectiveness of online learning.  
Meningkatkan kinerja SVM: Dampak berbagai teknik seleksi fitur pada akurasi prediksi Huizen, Lenny Margaretta; Ardima, Muhammad Basyier; Idris, Mochamad
AITI Vol 22 No 1 (2025)
Publisher : Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/aiti.v22i1.1-14

Abstract

Pada akreditasi Perguruan Tinggi, kelulusan mahasiswa memainkan peran penting sebagai salah satu kriteria penilaian. Prediksi kelulusan merupakan fokus utama untuk membantu institusi dalam menilai seorang mahasiswa lulus tepat waktu. Penelitian ini mengambil data historis dari mahasiswa yang telah lulus, yang diambil melalui kuesioner dari mahasiswa Program Studi Sistem Informasi dan Teknik Informatika Universitas Semarang. Metode seleksi fitur digunakan untuk menyeleksi atribut yang paling relevan pada prediksi kelulusan. Hasil seleksi ini diujikan menggunakan Algoritma Support Vector Machine (SVM). Tujuan utama dari penelitian ini adalah untuk mengevaluasi dampak seleksi fitur terhadap prediksi kelulusan. Hasil pengujian menunjukkan bahwa SVM dengan seleksi fitur menggunakan weight by relief mencapai akurasi sebesar 82%, presisi 83,42%, dan recall 80,83%. Sebaliknya, SVM tanpa menggunakan weight by relief menunjukkan akurasi 69,23%, presisi 70,83%, dan recall 67,86%. Penggunaan seleksi fitur berhasil mengurangi fitur dari 27 menjadi empat fitur yang paling berpengaruh.

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