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All Journal Jurnal Pendidikan Teknologi dan Kejuruan MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Prosiding Seminas Competitive Advantage Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Kursor Journal of Development Research JOIV : International Journal on Informatics Visualization Applied Information System and Management IJISTECH (International Journal Of Information System & Technology) Systemic: Information System and Informatics Journal ILKOMNIKA: Journal of Computer Science and Applied Informatics Jurnal Mnemonic International Journal of Advances in Data and Information Systems IJISTECH Jurnal Indonesia : Manajemen Informatika dan Komunikasi Jurnal Janitra Informatika dan Sistem Informasi Bulletin of Social Informatics Theory and Application Jurnal Indonesia : Manajemen Informatika dan Komunikasi
Yunifa Miftachul Arif
Department Of Informatics Engineering, Universitas Islam Negeri Maulana Malik Ibrahim Malang
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences

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PERILAKU TAKTIS UNTUK NON - PLAYER CHARACTERS DI GAME PEPERANGAN MENIRU STRATEGI MANUSIA MENGGUNAKAN FUZZY LOGIC DAN HIERARCHICAL FINITE STATE MACHINE Supeno Mardi Susiki Nugroho; Yunifa Miftachul Arif; Mochamad Hariadi; Mauridhi H Purnomo
Jurnal Ilmiah Kursor Vol 6 No 1 (2011)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Nowadays, the proliferation of game technology especially in intelligent human-like NPCs (Non-Player Characters) leads to more adaptive behavior of NPCs maneuvers. Providing smoother behaviors require comprehensive rules base which can respond to players behaviors. Addressing this requirement we propose NPCs which had tactical behaviors based on fuzzy logic. The fuzzy logic defines four type behaviors for the NPCs, which depend on NPC health, ammo, and distance of the enemy.Those behaviors implemented on two intelligent agents employed Hierarchical Finite State Machine to express the maneuver actions of NPC during combat scenes. Using First Person Game Engine, the performance of NPCs with fuzzy behavior compared with NPC without fuzzy behavior. The results of experiment showed the performance of the NPC with Fuzzy behavior outperform 80% better than the NPC without fuzzy behavior.
Game Promosi Wisata Kota Malang “Kakang Mbakyu” Dengan Menggunakan Decission Tree dan Hierarchy Finite State Fathurrahman; Yunifa Miftachul Arif
Systemic: Information System and Informatics Journal Vol. 6 No. 1 (2020): Agustus
Publisher : Program Studi Sistem Informasi Fakultas Sains dan Teknologi, UIN Sunan Ampel Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29080/systemic.v6i1.958

Abstract

Currently, the Tourism Sector in Indonesia is considered the most effective sector contributing to increasing the country's foreign exchange, Foreign exchange earnings were obtained from Indonesian tourism visits which surged and recorded as the highest compared to other countries in Southeast Asia, In particular in the city of Malang there was a significant increase in the number of tourists, based on data listed at www.malangkota.go.id in 2015 the number of tourists entering the city of Malang totaled 3,290,067 people, while 8,265 foreign tourists visited the following year to 3,987,074 for domestic tourists and 9,535 foreign tourists. In this research, Decission Tree algorithm was successfully implemented in the game by producing a time variable gain with a value of 2.01 and a gain point of 1.86 so that the time variable will be processed before the variable points to produce level jumps according to the ability of the player, and for the Hierarchy Finite State Machine, it proved to be successful with the NPC's moving behavior in accordance with the previously designed rules.
Tourism Destinations Popularity Rating In Malang Raya using Naive Bayes Classifier and Selection Sort Based on Twitter Word Polarity Yunifa Miftachul Arif; Mochammad Wahyu Firmansyah; Roro Inda Melani; S Supriyono
IJISTECH (International Journal of Information System and Technology) Vol 3, No 2 (2020): May
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (864.594 KB) | DOI: 10.30645/ijistech.v3i2.58

Abstract

The development of tourism today has been supported by advances in information technology that can make it easier for everyone to get information about tourist attractions. Technology plays an essential role in improving the tourism industry sector. During the tour, tourists usually share moments by uploading photos or making a status on social media related to their experience visiting a tourist site. Malang, which has various types of tourism, makes it a tourist destination. However, the number of tours in Malang makes tourists confused to choose the trip to be visited. Because of this, we need a system that can provide information in the form of popular tourist rankings. In this research, a system that can determine the ranking of tourist attractions in Malang Raya was made. The data used comes from social media user tweets on Twitter using the keyword name of tourist attractions in Malang. The Naive Bayes Classifier method is used to help tweet classification, and the Selection Sort method is used to help the ranking process of tourist attractions. The final results obtained in the Batu City tourism ranking resulted in an accuracy of 86.3%, while in the tourism rating the artificial type of Batu City produced an accuracy of 100%. The difference in accuracy occurs because there are the same positive values at several tourist attractions, so the Selection Sort method cannot work. Because of this, further research is needed for ranking methods that can rank with the same positive value to produce a better ranking of tourist attractions.
Performance of Multi-Criteria Recommender System Using Cosine-Based Similarity for Selecting Halal Tourism Rizqi Aulia Nadhifah; Yunifa Miftachul Arif; Hani Nurhayati; Linda Salma Angreani
Applied Information System and Management (AISM) Vol 5, No 2 (2022): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v5i2.25035

Abstract

Tourism is an activity where people or groups travel voluntarily for relaxation, seeking entertainment, or enjoy cultural diversity both within the city, outside the city, or even abroad. For traveling, information about halal tourism is essential that tourists must know. Tourists can contact a tour guide to find information and recommendations for halal tourism. However, it will cost quite a bit and need for a recommendation system to obtain recommendations and make it easier for tourists to determine which halal tourism to visit. This study aims to obtain the Multi-Criteria Recommender System's (MCRS) performance using cosine-based similarity to select halal tourism in Batu City. MCRS extends the traditional approach by using more than one scoring criteria to generate recommendations. The implementation of MCRS using cosine-based similarity succeeded in producing the five highest recommendations for halal tourist attractions, which were implemented in a game-based system. Through recommendation accuracy testing on two items, three items, four items, and five tourist attractions items, we obtained an average accuracy is 77,95%.
Selecting Tourism Site Using 6 As Tourism Destinations Framework Based Multi-Criteria Recommender System Yunifa Miftachul Arif; Duvan Deswantara Putra; Nauman Khan
Applied Information System and Management (AISM) Vol 6, No 1 (2023): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v6i1.25140

Abstract

Batu City is a place with many types of tourism and had many tourists in 2019. However, there was an imbalance of tourist attractions visited from the total number. Tourists are only fixated on famous tourist spots. Therefore, a recommendation system is needed that can provide recommendations for tourists. In this study, we use the Multi-Criteria Recommender System (MCRS) method based on the rating value between users to obtain recommendations from the system regarding selecting tourist destinations. The authors use the 6 As Tourism Destinations (6AsTD) framework for user assessment criteria in this study. The framework consists of six indicators that assess the success of tourism destinations, including attractions, accessibility, amenities, support services, activities, and available packages. The six components are considered the key to the success of a tourist destination under the marketing approach. This study aimed to obtain a recommendation system for selecting tourist destinations using the multi-criteria concept based on the 6AsTD framework. Based on the experimental results, the proposed method has an accuracy rate of up to 72%.
ANALISIS SENTIMEN ARTIKEL BERITA PEMILU BERBASIS METODE KLASIFIKASI Fathir; M. Amin Hariyadi; Yunifa Miftachul A
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 4 No. 2 (2023): Jurnal Indonesia : Manajemen Informatika dan Komunikasi (JIMIK)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) AMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v4i2.220

Abstract

The distribution of information in the form of online news is so massive in the wider community, that it is difficult to distinguish between haox news and positive news. So that a classification is needed regarding public sentiment about the implementation of elections using mainstream media news article data using 1064 dataset test data. The methods used in this study are the naive Bayes algorithm, the random forest algorithm, and the support vector machine algorithm. The test model uses smote where the performance results are carried out by the algorithm used using smote and not using smote, where random forest produces an accuracy of 91.88%, while without using a smote support vector machine it produces an accuracy of 92.05%.
Analysis of the Use of Artificial Neural Network Models in Predicting Bitcoin Prices Muhammad Sahi; Muhammad Faisal; Yunifa Miftachul Arif; Cahyo Crysdian
Applied Information System and Management (AISM) Vol 6, No 2 (2023): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v6i2.29648

Abstract

Bitcoin is one of the fastest-growing digital currencies or cryptocurrencies in the world. However, the highly volatile Bitcoin price poses a very extreme risk for traders investing in cryptocurrencies, especially Bitcoin. To anticipate these risks, a prediction system is needed to predict the fluctuations in cryptocurrency prices. Artificial Neural Network (ANN) is a relatively new model discovered and can solve many complex problems because the way it works mimics human nerve cells. ANN has the advantage of being able to describe both linear and non-linear models with a fairly wide range. This research aims to determine the best performance and level of accuracy of the ANN model using the Back-Propagation Neural Network (BPNN) algorithm in predicting Bitcoin prices. This study uses Bitcoin price data for the period 2020 to 2023 taken from the CoinDesk market. The results of this study indicate that the ANN model produces the best performance in the form of four input nodes, 12 hidden nodes, and one output node (4-12-1) with an accuracy rate of around 3.0617175%.
Non-Rating Recommender System for Choosing Tourist Destinations Using Artificial Neural Network Yunifa Miftachul Arif; Dyah Wardani; Hani Nurhayati; Norizan Mat Diah
Applied Information System and Management (AISM) Vol 6, No 2 (2023): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v6i2.26741

Abstract

The development of tourist destinations in Batu City makes it hard for the tourists to decide their destinations. The recommender system is a solution that provides a lot of information or tourist attraction data. Collaborative filtering is often used in recommender systems. However, it has drawbacks; one of which is the cold-start problem, where the system cannot recommend items to new users. It was caused by the new user who had no history of rating on any item, or the system had no information. This study aims to apply a non-rated travel destination recommendation system to address the cold-start problem for new users. We use a multi-layer perceptron or artificial neural networks to overcome the problem by training user preference data to produce high training accuracy. Based on four experiments in the training data, the network architecture shows 5 – 7 – 5 – 3 –14, which is the highest accuracy. The architecture uses five variables as inputs and three hidden layers, with each layer was activated using the ReLU activation function. The output layer produces 14 binary outputs and is activated using the sigmoid activation function. The system can give recommendations to new users using feedforward from test data with updated values in weights and biases. The test results from 46 test data showed an accuracy of 67.235%.
Classification of Students' Academic Performance Using Neural Network and C4.5 Model Sulika Sulika; Ririen Kusumawati; Yunifa Miftachul Arif
International Journal of Advances in Data and Information Systems Vol. 5 No. 1 (2024): April 2024 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v5i1.1311

Abstract

ducation involves deliberately creating an environment and learning process to empower students to fully utilize their academic and non-academic potential. It encompasses fostering spiritual qualities, religious understanding, self-discipline, cognitive abilities, and skills necessary for personal, societal, national, and state development. Madrasah Aliyah, in particular, emphasizes preparing participants for higher studies in areas of their interest, thereby showcasing their academic prowess. The evaluation of educational models like Neural Networks is crucial for ensuring their effectiveness in problem-solving. This involves testing and assessing the performance of the Neural Network model to ensure its accuracy and reliability. Similarly, the C4.5 method, based on condition data mining, is utilized to measure classification performance by assessing accuracy, precision, and recall. Research findings indicate that the neural network algorithm is more adept at accurately classifying students' academic abilities compared to the C4.5 algorithm. With an accuracy of 92.6% for the neural network algorithm and 80.6% for the C4.5 algorithm, it is evident that the former is more precise in determining the classification of students' academic abilities. This highlights the suitability of the neural network approach for classifying academic abilities in Madrasah Aliyah. Furthermore, the insights gained from this classification process can be extrapolated to benefit other madrasas.
Comparing neural network with linear Regression for stock market prediction Kurniawan, Fachrul; Arif, Yunifa Miftachul; Nugroho, Fresy; Ikhlayel, Mohammed
Bulletin of Social Informatics Theory and Application Vol. 7 No. 1 (2023)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v7i1.621

Abstract

There are both gains and losses possible in stock market investing. Brokerage firms' stock investments carry a higher risk of loss since their stock prices are not being tracked or analyzed, which might be problematic for businesses seeking investors or individuals. Thanks to progress in information and communication technologies, investors may now easily collect and analyze stock market data to determine whether to buy or sell. Implementing machine learning algorithms in data mining to obtain information close to the truth from the desired objective will make it easier for an individual or group of investors to make stock trades. In this study, we test hypotheses on the performance of a financial services firm's stock using various machine learning and regression techniques. The relative error for the neural network method is only 0.72 percentage points, while it is 0.78 percentage points for the Linear Regression. More training cycles must be applied to the Algortima neural network to achieve more accurate results.
Co-Authors AA Sudharmawan, AA Achmad Sabar, Achmad Ady Wicaksono Agariadne Dwinggo Samala Alfachruddin, M. Nabil Fahd Aristantia, Yuliana Bojic, Ljubisa Cahyo Crysdian Coelho, Diogo Pereira Dedy Kurnia Setiawan Duvan Deswantara Putra Dyah Wardani Fachrul Kurniawan Fathir Fathir Fathir Fathurrahman Firmansyah, Rezky Fresy Nugroho Hamid, Abdulhalim Hamid Salih Hani Nurhayati Howard, Natalie-Jane Ihsan, Afif Nuril Ikhlayel, Mohammed Imamudin, M. Janitra, Geovanni Azam Juniardi Nur Fadila Linda Salma Angreani Mauludiah, Siska Farizah Mauridhi H Purnomo Mochamad Hariadi Mochammad Wahyu Firmansyah Mokhamad Amin Hariyadi Muhammad Faisal Muhammad Faisal Muhammad Sahi Nauman Khan Norizan Mat Diah Putra, Dony Darmawan Rawas, Soha Ririen Kusumawati Rizqi Aulia Nadhifah Rony, Zahara Tussoleha Roro Inda Melani Safitri A Basid, Puspa Miladin Nuraida Setiyawan, Niko Heri Sulika Sulika Supeno Mardi S. N, Supeno Mardi Supeno Mardi Susiki Nugroho, Supeno Mardi Supriyono Tsoy, Dana Wahyuliningtyas, Lia Zainal Abidin Zulfiandri Zulfiandri