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Sistem Pakar Medis Berbasis Aturan Rekomendasi Penanganan Penyakit Tropis Mulyana, Sri; Wardoyo, Retantyo; Musdholifah, Aina
Prosiding SNATIKA Vol 3 (2015): Prosiding Snatika (Seminar Nasional Teknologi, Informasi, Komunikasi dan Aplikasinya)
Publisher : LPPM STIKI Malang

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

Sistem Pakar Medis Rekomendasi Penanganan berbasis aturan (SPMRP) dikembangkan sebagai alat bantu mendiagnosa gejala yang berkaitan dengan penyakit tropis yang diberikan, menunjukkan penyakit yang mungkin, dan penanganan yang mungkin didasarkan pada diagnosis. Namun pada tulisan ini, penulis memfokuskan pada salah satu penyakit tropis, yaitu tuberkulosis. SPMRP menggunakan basis pengetahuan yang terdiri dari tiga struktur pengetahuan, yaitu gejala, penyakit dan rekomendasi penanganan. Sistem SPMRP memiliki antarmuka yang user-friendly sehingga memudahkan pengguna untuk memberikan atau mendapatkan informasi ke / dari SPMRP selama run-time. Berbagai gejala penyakit tropis disimpan di pusat diagnostik dan pasien memilih tanda dan gejala dari daftar drop-down. Data-data ini kemudian digunakan oleh SPRMP untuk melakukan diagnosis dan rekomendasi penanganan. Mesin inferensi SPMRP menggunakan mekanisme forward chaining untuk mencari basis pengetahuan gejala penyakit dan rekomendasi penanganan yang terkait dan sesuai dengan data yang diberikan oleh pasien. SPMRP dibagun dengan tujuan membantu orang-orang yang tidak memiliki atau kesulitan akses ke fasilitas medis dan juga oleh mereka yang membutuhkan solusi pertolongan pertama sebelum ke konsultan medis. Selain itu, dapat digunakan untuk pembelajaran atau sosialisasi tentang penyakit truberkulosis. Dengan demikian, SPMRP akan mengurangi beban kerja dokter selama konsultasi, memmbantu pihak pemerintah dan meringankan masalah lain yang terkait dengan konsultasi penyakit tuberkulosis dan penanganannya

FVEC feature and Machine Learning Approach for Indonesian Opinion Mining on YouTube Comments Musdholifah, Aina; Rinaldi, Ekki
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 5: EECSI 2018
Publisher : IAES Indonesia Section

Mining opinions from Indonesian comments from YouTube videos are required to extract interesting patterns and valuable information from consumer feedback. Opinions can consist of a combination of sentiments and topics from comments. The features considered in the mining of opinion become one of the important keys to getting a quality opinion. This paper proposes to utilize FVEC and TF-IDF features to represent the comments. In addition, two popular machine learning approaches in the field of opinion mining, i.e., SVM and CNN, are explored separately to extract opinions in Indonesian comments of YouTube videos. The experimental results show that the use of FVEC features on SVM and CNN achieves a very significant effect on the quality of opinions obtained, in term of accuracy.

CASE BASE REASONING (CBR) AND DENSITY BASED SPATIAL CLUSTERING APPLICATION WITH NOISE (DBSCAN)-BASED INDEXING IN MEDICAL EXPERT SYSTEMS Santoso, Herdiesel; Musdholifah, Aina
Khazanah Informatika Vol. 5 No. 2 December 2019
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v5i2.8323

Case-based Reasoning (CBR) has been widely applied in the medical expert systems. CBR has computational time constraints if there are too many old cases on the case base. Cluster analysis can be used as an indexing method to speed up searching in the case retrieval process. This paper propose retrieval method using Density Based Spatial Clustering Application with Noise (DBSCAN) for indexing and cosine similarity for the relevant cluster searching process. Three medical test data, that are malnutrition disease data, heart disease data and thyroid disease data, are used to measure the performance of the proposed method. Comparative tests conducted between DBSCAN and Self-organizing maps (SOM) for the indexing method, as well as between Manhattan distance similarity, Euclidean distance similarity and Minkowski distance similarity for calculating the similarity of cases. The result of testing on malnutrition and heart disease data shows that CBR with cluster-indexing has better accuracy and shorter processing time than non-indexing CBR. In the case of thyroid disease, CBR with cluster-indexing has a better average retrieval time, but the accuracy of non-indexing CBR is better than cluster indexing CBR. Compared to SOM algorithm, DBSCAN algorithm produces better accuracy and faster process to perform clustering and retrieval. Meanwhile, of the three methods of similarity, the Minkowski distance method produces the highest accuracy at the threshold ? 90.

The Ontology-Based Methodology Phases To Develop Multi-Agent System (OmMAS) Yunianta, Arda; Musdholifah, Aina; Haviluddin, Nataniel Dengen; Yusof, Omar Obarukab Norazah; Jayadiyanti, Herlina; Othman, Mohd Shahizan
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 4: EECSI 2017
Publisher : IAES Indonesia Section

Semantic aspect on methodology phase is a significantÂ issue Â to Â develop Â multi-agentÂ system inÂ the Â current days.Â Â There are a lot of methodologies to develop multi-agent system, but the current problem is how to choose the best methodology phase to develop current multi-agent system. The development of multi-agent system currently is to be more complex and difficult. Many aspects that contains on multi-agent system, the one of the famous issue now is about semantic aspect on multi-agent system. The old methodology phases are not suitable to develop current multi-agent system. Nowadays, many researchers start to improve and customize the obsolete methodology to adjust with the current needed. There are two research steps contains in this paper, the first step is to review and criticize previous methodologies especially about MOMA (Methodology for Developing Ontology-Based Multi-Agent System) was introduced in 2013. The second step is the main contribution of this paper is to improve previous methodology phases with the new methodology phases named OmMas (The Ontology-Based Methodology phases to Develop Multi-Agent System), and using semantic aspect as theÂ main focus of this methodology. The result of this research is improved ontology- based methodology phases as a representation of semantic aspect on the ontology development process.Â

An Optimization Model for Environmental Ergonomics Assessment in Bioproduction of Food SMEs Mirwan Ushada; Hani Febri Mustika; Aina Musdholifah; Tsuyoshi Okayama
HAYATI Journal of Biosciences Vol. 27 No. 4 (2020): October 2020
Publisher : Bogor Agricultural University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.4308/hjb.27.4.296

Environmental ergonomics in bioproduction of food Small Medium-sized Enterprises (SMEs) become a concern and need to be optimized. An optimization model was developed using a Genetic Algorithm (GA). The weight of an Artificial Neural Network Model was used as a fitness function for GA. The research objectives were: 1) To design an environmental ergonomic assessment system for bioproduction of Food SMEs, 2) To develop an optimization model for environmental ergonomic assessment using a Genetic Algorithm. GA is utilized to search optimal set points of environmental ergonomics based on the predicted fitness values. Each chromosome of GA represents the environmental ergonomics value. The parameters were heart rate, bioproduction temperature, distribution of bioproduction relative humidity and light intensity. The target of the optimization model was the bioproduction temperature set points. The research result indicated the model generated optimum values of environmental ergonomics parameter in bioproduction of food SMEs. The parameters could be used to provide standard workplace environment for the sustainability of food SMEs.

Book Recommender System Using Genetic Algorithm and Association Rule Mining Hani Febri Mustika; Aina Musdholifah
Computer Engineering and Applications Journal Vol 8 No 2 (2019)
Publisher : Universitas Sriwijaya

Recommender system aims to provide on something that likely most suitable and attractive for users. Many researches on the book recommender system for library have already been done. One of them used association rule mining. However, the system was not optimal in providing recommendations that appropriate to the user's preferences and achieving the goal of recommender system. This research proposed a book recommender system for the library that optimizes association rule mining using genetic algorithm. Data used in this research has taken from Yogyakarta City Library during 2015 until 2016. The experimental results of the association rule mining study show that 0.01 for the greatest value of minimum support and 0.4359 for the average confidence value due to a lot of data and uneven distribution of data. Furthermore, other results are 0.499471 for the average of Laplace value, 30.7527 for the average of lift value and 1.91534252 for the average of conviction value, which those values indicate that rules have good enough level of confidence, quite interesting and dependent which indicates existing relation between antecedent and consequent. Optimization using genetic algorithm requires longer execution time, but it was able to produce book recommendations better than only using association rule mining. In Addition, the system got 77.5% for achieving the goal of recommender system, namely relevance, novelty, serendipity and increasing recommendation diversity.

Identification of Rice Variety Using Geometric Features and Neural Network Wahyu Srimulyani; Aina Musdholifah
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 13, No 3 (2019): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.48203

Indonesia has many food varieties, one of which is rice varieties. Each rice variety has physical characteristics that can be recognized through color, texture, and shape. Based on these physical characteristics, rice can be identified using the Neural Network. Research using 12 features has not optimal results. This study proposes the addition of geometry features with Learning Vector Quantization and Backpropagation algorithms that are used separately.The trial uses data from 9 rice varieties taken from several regions in Yogyakarta. The acquisition of rice was carried out using a camera Canon D700 with a kit lens and maximum magnification, 55 mm. Data sharing is carried out for training and testing, and the training data was sharing with the quality of the rice. Preprocessing of data was carried out before feature extraction with the trial and error thresholding process of segmentation. Evaluation is done by comparing the results of the addition of 6 geometry features and before adding geometry features.The test results show that the addition of 6 geometry features gives an increase in the value of accuracy. This is evidenced by the Backpropagation algorithm resulting in increased accuracy of 100% and 5.2% the result of the LVQ algorithm.

Online Learning Video Recommendation System Based on Course and Sylabus Using Content-Based Filtering Faisal Ramadhan; Aina Musdholifah
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 15, No 3 (2021): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.65623

Learning using video media such as watching videos on YouTube is an alternative method of learning that is often used. However, there are so many learning videos available that finding videos with the right content is difficult and time-consuming. Therefore, this study builds a recommendation system that can recommend videos based on courses and syllabus. The recommendation system works by looking for similarity between courses and syllabus with video annotations using the cosine similarity method. The video annotation is the title and description of the video captured in real-time from YouTube using the YouTube API. This recommendation system will produce recommendations in the form of five videos based on the selected courses and syllabus. The test results show that the average performance percentage is 81.13% in achieving the recommendation system goals, namely relevance, novelty, serendipity and increasing recommendation diversity.

Case Base Reasoning (CBR) and Density Based Spatial Clustering Application with Noise (DBSCAN)-based Indexing in Medical Expert Systems Herdiesel Santoso; Aina Musdholifah
Khazanah Informatika Vol. 5 No. 2 December 2019
Publisher : Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v5i2.8323

Case-based Reasoning (CBR) has been widely applied in the medical expert systems. CBR has computational time constraints if there are too many old cases on the case base. Cluster analysis can be used as an indexing method to speed up searching in the case retrieval process. This paper propose retrieval method using Density Based Spatial Clustering Application with Noise (DBSCAN) for indexing and cosine similarity for the relevant cluster searching process. Three medical test data, that are malnutrition disease data, heart disease data and thyroid disease data, are used to measure the performance of the proposed method. Comparative tests conducted between DBSCAN and Self-organizing maps (SOM) for the indexing method, as well as between Manhattan distance similarity, Euclidean distance similarity and Minkowski distance similarity for calculating the similarity of cases. The result of testing on malnutrition and heart disease data shows that CBR with cluster-indexing has better accuracy and shorter processing time than non-indexing CBR. In the case of thyroid disease, CBR with cluster-indexing has a better average retrieval time, but the accuracy of non-indexing CBR is better than cluster indexing CBR. Compared to SOM algorithm, DBSCAN algorithm produces better accuracy and faster process to perform clustering and retrieval. Meanwhile, of the three methods of similarity, the Minkowski distance method produces the highest accuracy at the threshold ≥ 90.

The Ontology-Based Methodology Phases To Develop Multi-Agent System (OmMAS) Arda Yunianta; Aina Musdholifah; Nataniel Dengen Haviluddin; Omar Obarukab Norazah Yusof; Herlina Jayadiyanti; Mohd Shahizan Othman
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 4: EECSI 2017
Publisher : IAES Indonesia Section

Semantic aspect on methodology phase is a significant issue to develop multi-agent system in the current days. There are a lot of methodologies to develop multi-agent system, but the current problem is how to choose the best methodology phase to develop current multi-agent system. The development of multi-agent system currently is to be more complex and difficult. Many aspects that contains on multi-agent system, the one of the famous issue now is about semantic aspect on multi-agent system. The old methodology phases are not suitable to develop current multi-agent system. Nowadays, many researchers start to improve and customize the obsolete methodology to adjust with the current needed. There are two research steps contains in this paper, the first step is to review and criticize previous methodologies especially about MOMA (Methodology for Developing Ontology-Based Multi-Agent System) was introduced in 2013. The second step is the main contribution of this paper is to improve previous methodology phases with the new methodology phases named OmMas (The Ontology-Based Methodology phases to Develop Multi-Agent System), and using semantic aspect as the main focus of this methodology. The result of this research is improved ontology- based methodology phases as a representation of semantic aspect on the ontology development process.

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