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All Journal Informatik : Jurnal Ilmu Komputer Informatika BERNAS: Jurnal Pengabdian Kepada Masyarakat International Journal of Computer and Information System (IJCIS) Jurnal Abdimas ADPI Sains dan Teknologi Journal of Science and Technology Journal of Student Development Information System (JoSDIS) SAGA: Journal of Technology and Information Systems Edu Komputika Journal
Fithratul Zalmi, Wahyuni
Universitas Sam Ratulangi, Manado
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Prediksi Biaya Pemakaian Listrik Dengan Metode Logika Fuzzy Mamdani Zalmi, Wahyuni Fithratul; Pang, David; Nurgraha, Mahendra Kusuma
Jurnal Informatika Vol 12, No 3 (2024): INFORMATIKA
Publisher : Fakultas Sains & Teknologi, Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/informatika.v12i3.6337

Abstract

Understanding the electricity tariff class is needed to predict how much usage costs or electricity bills that must be incurred by customers. Each power capacity has a different basic electricity tariff based on the class, household, business and industry. Fuzzy logic can solve problems with imprecise and heuristic data by producing accurate conclusions. The conclusion obtained from the Fuzzy Logic results is expected to be a consideration for customers in predicting electricity usage costs based on electricity tariff groups and customer electrical power capacity. The proposed research uses the Mamdani Fuzzy Logic Method, the use of this method aims to facilitate customers in predicting the cost of electricity usage based on the electricity tariff class and the customer's electric power capacity. The output of this research is the result of predicting the cost of electricity usage based on the power group in residential homes. The higher the tariff group, the number of kWh, electronic goods, and usage time, the higher the predicted energy consumption used. The results obtained from adjusting the fuzzy input antecedents and fuzzy values, namely [900;850;30;20], then for Type_Tariff Power 900, Total_KWh 850, Electronics 30, and Time_Use 20 is 986. Then the cost that must be incurred in the example case above is 1,333,072 rupiah.
Creation of a Virtual Laboratory for Collision Dynamics Educational Tool with Integrated Collision Algorithm Yusupa, Ade; Kalua, Aditya Lapu; Zalmi, Wahyuni Fithratul; Kurnia, Rahmi Putri; Tarigan, Victor
Edu Komputika Journal Vol. 11 No. 2 (2024): Edu Komputika Journal
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edukom.v11i2.10612

Abstract

Conventional physics laboratories often suffer from limitations in terms of equipment availability and safety, which hinders optimal learning of collision dynamics concepts. This research aims to develop a virtual laboratory based on collision algorithm to simulate perfect collision as an alternative solution in physics learning. The development uses the ADDIE model, which includes the stages of analysis, design, development, implementation, and evaluation. The collision algorithm was implemented using ActionScript 3, with interpolation allowing for more accurate collision detection at high speeds. The validation results show that the simulation is in line with the law of conservation of momentum and kinetic energy and is consistent with analytical solutions from MATLAB and Python. Functionality testing was conducted by 20 students, and the results showed that the use of this virtual laboratory significantly improved their concept understanding, with the average improvement ranging from 24% to 56%. Students also reported that this virtual laboratory is more interactive and interesting, thus increasing their learning motivation. The conclusion of this study is that the collision algorithm-based virtual laboratory is effective as a physics learning media and can be adopted more widely in technology-based education, especially to understand complex physics concepts more deeply.
Rancang Bangun Aplikasi Mobile Perantau Minang Berdomisili Di Kota Manado Dengan Metode Cluster Analysis Zalmi, Wahyuni Fithratul; Syahputra, Rendy; Nugraha, Mahendra Kusuma
Journal of Student Development Information System (JoSDIS) Vol 5, No 1: JoSDIS | Januari 2025
Publisher : Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/josdis.v5i1.8006

Abstract

Applications are software developed to process data and generate information. There are three types of applications, namely desktop applications, web applications, and mobile applications. Mobile applications allow users to access information and resources anytime and anywhere, as long as connected to the internet. Minang nomads are Minangkabau people who live outside West Sumatra, both in Indonesia and abroad. In the city of Manado, Minang nomads often find it difficult to obtain accurate information, such as the number of Family Cards, ethnic or clan groupings, and other important information such as address, occupation, account number, and blood type. With this application, the data of Minang nomads can be centralized and make it easier for them to share information. This application aims to improve relations between fellow Minang nomads in Manado City and strengthen the local economy. Some of the main features of this application include the ease of finding information on the schedule of activities, the number and grouping of tribes, addresses, blood types, as well as the work of Minang nomads.
Penerapan Convolutional Neural Network (CNN) untuk Klasifikasi Penyakit Daun Tomat Fithratul Zalmi, Wahyuni; Hari Saputro, Pujo; Sitanggang, Jonathan; Leatemia, Kevin
Informatik : Jurnal Ilmu Komputer Vol 21 No 2 (2025): Agustus 2025
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52958/iftk.v21i2.11094

Abstract

Penyakit pada daun tomat merupakan salah satu masalah utama dalam pertanian yang dapat menyebabkan penurunan hasil panen dan kualitas tanaman. Deteksi dini dan akurat terhadap penyakit ini sangat penting untuk menghindari kerugian yang lebih besar. Dalam penelitian ini, kami mengembangkan sistem klasifikasi penyakit daun tomat menggunakan teknik deep learning dengan arsitektur Convolutional Neural Network (CNN). Dataset yang digunakan terdiri dari gambar daun tomat dalam beberapa kategori penyakit, yang kemudian diproses menggunakan data augmentation untuk meningkatkan jumlah dan variasi data pelatihan. Model CNN yang dibangun terdiri dari beberapa lapis konvolusi dan max-pooling, diikuti oleh lapis dens (dense layer) untuk mengklasifikasikan gambar ke dalam 10 kategori penyakit. Hasil penelitian menunjukkan bahwa model yang dikembangkan mencapai akurasi sekitar 95.84% pada dataset validasi, dengan kemampuan yang baik dalam membedakan berbagai jenis penyakit. Analisis matriks kekacauan (confusion matrix) menunjukkan bahwa model memiliki performa yang konsisten dalam mengklasifikasikan penyakit, meskipun ada beberapa kesalahan klasifikasi pada kategori tertentu. Sistem ini dapat menjadi alat bantu yang efektif bagi petani dan peneliti untuk mendeteksi penyakit daun tomat secara akurat dan efisien.
Performance Testing of KNN and Logistic Regression Algorithms in Classifying Heart Disease Susceptibility Saputro, Pujo Hari; Zalmi, Wahyuni Fithratul; Syahputra, Rendy
International Journal of Computer and Information System (IJCIS) Vol 4, No 4 (2023): IJCIS : Vol 4 - Issue 4 - 2023
Publisher : Institut Teknologi Bisnis AAS Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/ijcis.v4i4.133

Abstract

The annual global death toll due to cardiovascular diseases, which fall into the category of heart and blood vessel disorders, reaches 17.9 million lives. This undoubtedly requires more attention in order to anticipate the potential risk of heart attacks that can affect anyone at any time. Data analysis or data mining approaches have become a significant contribution in the field of information technology to provide valuable information regarding the risk of heart diseases. Data analysis using the K-Nearest Neighbor and Logistic Regression algorithms is expected to provide information related to the susceptibility category for heart diseases, such as age susceptibility, gender, cholesterol levels, and so on. With the information obtained from this data analysis, it is hoped that it can serve as a reference and consideration for individuals to be more vigilant in maintaining their health. The results indicate that the highest correlation with susceptibility to heart disease is based on a person's age and their body weight. The correlation coefficient between these two variables is 0.37, suggesting a relationship between a person's age and their body weight, which can make them more susceptible to heart disease. Testing with both algorithms shows a high level of accuracy, with K-Nearest Neighbor achieving an accuracy rate of 0.95, while Logistic Regression has an accuracy of 0.96.
Co-Authors AA Sudharmawan, AA Ade Yusupa Bernad J. D. Sitompul David Pang Djesmedi, Dinda Firdalius, Fitri Fitri Firdalius Hari Saputro, Pujo kurnia, rahmi putri Leatemia, Kevin Mahendra Kusuma Nugraha Pujo Hari Saputro Reinhard Komansilan Rendy Syahputra Sastra Wandi Nduru Sedihati Kayan Lumbangaol Sitanggang, Jonathan SY, Yulia Jihan Victor Tarigan