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All Journal CogITo Smart Journal JURNAL REKAYASA TEKNOLOGI INFORMASI Sebatik Building of Informatics, Technology and Science Indonesian Journal of Data and Science TEPIAN Ilmu Komputer untuk Masyarakat
Khoirunnita, Aulia
Amikom University
Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Mathematics Other

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Decision Support System for Wedding Package Using Multi-Objective Optimization of Ratio Analysis Method Astuti, Indah Fitri; Kridalaksana, Awang Harsa; Alex, Rasni; Fitri, Dewi; Cahyadi, Dedy; Khoirunnita, Aulia
TEPIAN Vol. 5 No. 2 (2024): June 2024
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tepian.v5i2.2995

Abstract

Some   of the problems in preparing a wedding day are determining the venue, event concept, concept and others are very time-consuming. Wedding organizers are an option to overcome these problems; one of the wedding organizers in Samarinda is Galeri Shella, which has various wedding packages with facilities and prices, making it difficult for brides-to-be. To make it easier to make the right wedding package decision, a decision support system is made. The decision aims to provide wedding package recommendations with criteria that can be chosen by the bride and groom and their budget. The method used in this system is Multi-Objective Optimization of Ratio Analysis (MOORA) using six criteria catering, venue, decoration, documentation, makeup and price, as a reference calculation that can produce the best wedding package recommendations according to the wishes of the bride and groom. The research results show that the system functions well, is easy to use, and makes it easier for brides to choose wedding packages. From the results of accuracy testing, it is known that the results of manual calculations and the system make no difference, and this study obtained an accuracy value of 100%.
Geographical Information System of Chocolate Plantation Locations in Berau District Using QGIS Web Khoirunnita, Aulia; Ramadhani, Fajar; Andrea, Reza
TEPIAN Vol. 5 No. 1 (2024): March 2024
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tepian.v5i1.3024

Abstract

The cultivation of cocoa plants in Berau Regency has been carried out traditionally with pre-conflict productivity of 700-800 kg/ha, but after the conflict and attacks PBK (Cocoa Fruit Borer) was reported to be only half. The yield quality is also low due to the pest attack and minimum post-harvest treatment. In addition, farmer institutions to be able to carry out activities in the garden together have not been well formed. According to Development Planning Agency at Sub-National Level of Berau Regency, in 2009 the area of cocoa plants in Berau Regency covered an area of 8,644 ha with a production of 2,362 tons. The area is spread across eight districts. With so many chocolate plantations in Berau regency, it certainly makes buyers or visitors, both from related ninas and individuals, become overwhelmed to find the location of chocolate plantations. Therefore, an application is needed to facilitate the search for location and data about the location you want to visit. Geographic Information Systems (GIS) or also known as Geographic Information Systems (GIS) have recently experienced significant developments along with the advancement of geographic information technology. GIS is a computer-based information system that combines map elements (geographical) and information about the map (attribute data) designed to obtain, process, manipulate, analyze, demonstrate and display special data to complete planning, processing and research problems. With the web-based designed GIS application, it is able to solve problems related to the vast chocolate plantation in Berau.
The Bajau-Tainment: An AI-Powered Puzzle Game for Learning the Bajau Language Using Finite State Machine and Shuffle Techniques Khoirunnita, Aulia; Alex, Rasni; Rosmasari , Rosmasari
TEPIAN Vol. 5 No. 3 (2024): September 2024
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tepian.v5i3.3140

Abstract

The research titled "Bajau-tainment" Educational Game (Edu-game) is a research project that focuses on the development of a Puzzle Game designed to improve memory, specifically in language learning. In this game, players must arrange randomized letters to form a word in the Bajau language. This research applies the shuffle random algorithm, which aims to ensure that the arrangement of letters is always shuffled, making the gameplay dynamic, non-monotonous, and engaging. AI Artificial Intelligence (AI) technology will also be applied in this research. Using the Finite State Machine (FSM) model method, the game will feature a game agent character that will accompany children during gameplay, like how a friend accompanies students in a classroom. This virtual friend can display emotions such as happiness or sadness based on the game environment. The research aims to make this edu-game more appealing and interactive for children. The AI game agent will act as a friend who accompanies the child throughout the game.
Penerapan Algoritma Minimax dan Alpha Beta Pruning Dalam Permainan Tactical Role-Playing Game Febryan, Raihan; Firdaus, Muhammad Bambang; Khoirunnita, Aulia; Saputra, Muhammad Fawaz; Wardhana, Reza; Putra, Gubtha Mahendra
Jurnal Rekayasa Teknologi Informasi (JURTI) Vol 9, No 4 (2025): Jurnal Rekayasa Teknologi Informasi (JURTI)
Publisher : Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jurti.v9i4.25007

Abstract

Tactical Role-Playing Game (TRPG) merupakan permainan strategi berbasis giliran yang menggabungkan elemen role-playing dan pengambilan keputusan taktis pada papan/grid, sehingga kualitas tantangan permainan sangat dipengaruhi oleh kecerdasan buatan (AI) dalam menentukan langkah optimal. Penelitian ini menerapkan algoritma Minimax sebagai pengambil keputusan AI dan mengoptimalkannya menggunakan Alpha-Beta Pruning untuk mempercepat proses pencarian tanpa mengubah keputusan pada kedalaman yang sama. Evaluasi dilakukan melalui pengujian performa keputusan AI pada permainan TRPG berbasis papan 8×8 yang memiliki atribut HP/MP dan multi-aksi (move, attack, magic attack, dan end turn). Hasil pengujian pada depth = 5 menunjukkan bahwa Minimax tanpa pruning menghasilkan waktu eksekusi lebih dari 6 detik per langkah dengan perluasan node rata-rata 182.400, sedangkan penerapan Alpha-Beta Pruning menurunkan waktu menjadi sekitar 2-3 detik dengan rata-rata expanded nodes 61.700. Pengujian variasi depth juga menunjukkan bahwa kedalaman pencarian berpengaruh langsung terhadap responsivitas permainan; depth rendah lebih responsif, sedangkan depth lebih tinggi meningkatkan beban komputasi dan berpotensi menyebabkan keterlambatan. Dengan demikian, optimasi Alpha-Beta Pruning terbukti meningkatkan efisiensi pengambilan keputusan AI pada TRPG dan membuat permainan lebih responsif pada perangkat dengan sumber daya terbatas.
Hybrid Feature Selection with Metaheuristics for Improving the Accuracy of Diabetes Disease Prediction Khamidah, Ida Maratul; Ramadhani, Suci; Khoirunnita, Aulia
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9541

Abstract

Early diagnosis of diabetes mellitus is crucial to prevent severe complications and reduce long-term healthcare costs, making accurate and efficient predictive models an important research focus in medical data analytics. However, one of the main challenges in diabetes prediction lies in the presence of irrelevant and redundant features within medical datasets, which can degrade classification accuracy, increase computational complexity, and reduce model generalizability. To address this issue, this study proposes a Hybrid Feature Selection (HFS) approach that integrates filter-based methods and meta-heuristic optimization to identify an optimal subset of features for diabetes prediction. In the proposed framework, statistical filter techniques combining Chi-square and Mutual Information are first employed to rank and reduce feature dimensionality by selecting the most relevant attributes. Subsequently, a Genetic Algorithm (GA) is applied to further optimize the feature subset by maximizing classification accuracy while minimizing the number of selected features. The effectiveness of the proposed HFS approach is evaluated using the Pima Indian Diabetes Dataset, consisting of 768 instances and 8 clinical features, and tested across multiple machine learning classifiers, including Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and XGBoost. Experimental results demonstrate that the proposed HFS significantly improves predictive performance compared to baseline models without feature selection. Specifically, the Random Forest classifier achieved the highest accuracy of 79.22%, compared to 74.03% in the baseline model, representing an improvement of approximately 5.2%. Additionally, notable improvements were observed in F1-score and AUC, with AUC increasing from 0.8336 to 0.8403. Beyond accuracy gains, the proposed method reduced feature dimensionality from 8 to 5 features, resulting in lower computational cost and faster model training time. These findings indicate that the hybrid integration of filter-based selection and meta-heuristic optimization provides a robust and efficient solution for feature selection in medical prediction tasks. Overall, the proposed HFS framework offers a promising approach for developing accurate, efficient, and reliable decision-support systems for early diabetes diagnosis.
Co-Authors Asro Nasiri Awang Harsa Kridalaksana Dedy Cahyadi, Dedy Dewi Fitri Ema Utami Erna Utami Fajar Ramadhani Febryan, Raihan Firdaus, Muhammad Bambang Harpad, Bartolomius Husmul Beze, Husmul Ida Maratul Khamidah Indah Fitri Astuti, Indah Fitri Iwan Muhamad Ramdan Jamil, Muh Kusnawi Kusnawi Maringka, Raissa Maringka, Rodney Giovanni Nanda, Arya Nur Rahmad , Didi Nur Rohmah Nurul Ikhsan Prafanto, Anton Putra, Gubtha Mahendra Rasni Alex Reza Andrea Rodney Maringka Rosmasari Rosmasari , Rosmasari Rosmasari Rosmasari, Rosmasari Rudito Rudito Saputra, Muhammad Fawaz Suci Ramadhani, Suci Wardhana, Reza Wing Wahyu Winarno