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Sri Hartati
Departemen Ilmu Komputer dan Elektronika, FMIPA UGM, Yogyakarta
Computer Science & IT Control & Systems Engineering

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Case-Based Reasoning untuk Diagnosa Penyakit THT (Telinga Hidung dan Tenggorokan) Tedy Rismawan; Sri Hartati
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 6, No 2 (2012): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.2154

Abstract

AbstrakCase-Based Reasoning (CBR) merupakan sistem penalaran komputer yang menggunakan pengetahuan lama untuk mengatasi masalah baru.CBR memberikan solusi terhadap kasus baru dengan melihat kasus lama yang paling mendekati kasus baru. Hal ini akan sangat bermanfaat karena dapat menghilangkan kebutuhan untuk mengekstrak model seperti yang dibutuhkan oleh sistem berbasis aturan. Penelitian ini mencoba untuk membangun suatu sistem Penalaran Berbasis Kasus untuk melakukan diagnosa penyakit THT (Telinga, Hidung dan Tenggorokan). Proses diagnosa dilakukan dengan cara memasukkan kasus baru (target case) yang berisi gejala-gejala ang akan didiagnosa ke dalam sistem, kemudian sistem akan melakukan proses indexing dengan metode backpropagation untuk memperoleh indeks dari kasus baru tersebut. Setelah memperoleh indeks, sistem selanjutnya melakukan proses perhitungan nilai similarity antara kasus baru dengan basis kasus yang memiliki indeks yang sama menggunakan metode cosine coefficient. Kasus yang diambil adalah kasus dengan nilai similarity paling tinggi. Jika suatu kasus tidak berhasil didiagnosa, maka akan dilakukan revisi kasus oleh pakar. Kasus yang berhasil direvisi akan disimpan ke dalam sistem untuk dijadikan pengetahuan baru bagi sistem. Hasil penelitian menunjukkan sistem penalaran berbasis kasus untuk mendiagnosa penyakit THT ini membantu paramedis dalam melakukan diagnosa. Hasil uji coba sistem terhadap 111 data kasus uji, terdapat 9 kasus yang memiliki nilai similarity di bawah 0.8.  Kata kunci—case-based reasoning, indexing, similarity, backpropagation, cosine coefficient Abstract Case-Based Reasoning (CBR) is a reasoning system that uses old knowledge to solve new problem. CBR provides solutions to new cases by looking at old case that comes closest to the new case. It will be very useful because it eliminates the need to extract the model as required by the rule-based systems. This studytriestoestablisha system forCBR for diagnosingdiseasesof ENT.Diagnosisprocessis done byinsertinga new casethat containsthe symptoms ofthe disease to bediagnosed, thenthe system willdo theindexingprocess with backpropagation method toobtainan indexofnewcases. Afterthat, the systemdo thecalculation of the valueof similaritybetweenthe newcasebycasebasiswhichhas thesame indexwithnew cases using cosine coefficient method. The casetaken isthe casewiththe highestsimilarityvalue. If acaseis not successfullydiagnosed, thecasewillbe revisedby theexperts and it can be used asnew knowledgefor thesystem. The results showedcase-basedreasoningsystemtodiagnosediseasesof ENTcan helpparamedicsin performingdiagnostics. The test results of 111 data test cases, obtained 9 cases that have similarity values below 0.8. Keywords—case-based reasoning, indexing, similarity, backpropagation, cosine coefficient
Case-Based Reasoning untuk Diagnosis Penyakit Jantung Eka Wahyudi; Sri Hartati
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 11, No 1 (2017): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.15523

Abstract

Case Based Reasoning (CBR) is a computer system that used for reasoning old knowledge to solve new problems. It works by looking at the closest old case to the new case. This research attempts to establish a system of CBR  for diagnosing heart disease. The diagnosis process  is done by inserting new cases containing symptoms into the system, then  the similarity value calculation between cases  uses the nearest neighbor method similarity, minkowski distance similarity and euclidean distance similarity.            Case taken is the case with the highest similarity value. If a case does not succeed in the diagnosis or threshold <0.80, the case will be revised by experts. Revised successful cases are stored to add the systemknowledge. Method with the best diagnostic result accuracy will be used in building the CBR system for heart disease diagnosis.            The test results using medical records data validated by expert indicate that the system is able to recognize diseases heart using nearest neighbor similarity method, minskowski distance similarity and euclidean distance similarity correctly respectively of 100%. Using nearest neighbor get accuracy of 86.21%, minkowski 100%, and euclidean 94.83%
Sistem Penjadwalan Pertandingan Pencak Silat Berbasis Algoritma Genetika Ari Kusuma Wardana; Sri Hartati
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 11, No 2 (2017): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.24214

Abstract

Genetic Algorithm is one of famous algorithm and often used in many sector. Usually genetic algoritm is used in solution searching about complex problems. Pencak silat macth scheduling is a complex scheduling and needs a lot of time to made it. Objective this research implements a genetic algorithm as an algorithm which can solve the problem of pencak silat  macth scheduling and can satisfy all of hard constraint and minimize soft constraint. In this research genetic algorithm roles as algorithm which solves pencak silat mach scheduling problems in Pimda 02 Tak Suci Bantul. Population which produced by genetic algorithm represents solution alternatives which offered. Best chromosome in a population represents macth scheduling solution. This solution is sequence of match partai based on rules of pencak silat match scheduling. This research produces best fitness value ever in each generation is 1. More and more chromosom number and more and more generation number will make batter solution and batter fitness value. This research is expected helping pencak silat match committes make a pencak silat schedule in pencak silat championship.
Adaptive Unified Differential Evolution for Clustering Maulida Ayu Fitriani; Aina Musdholifah; Sri Hartati
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 12, No 1 (2018): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.27871

Abstract

Various clustering methods to obtain optimal information continues to evolve one of its development is Evolutionary Algorithm (EA). Adaptive Unified Differential Evolution (AuDE), is the development of Differential Evolution (DE) which is one of the EA techniques. AuDE has self adaptive scale factor control parameters (F) and crossover-rate (Cr).. It also has a single mutation strategy that represents the most commonly used standard mutation strategies from previous studies.The AuDE clustering method was tested using 4 datasets. Silhouette Index and CS Measure is a fitness function used as a measure of the quality of clustering results. The quality of the AuDE clustering results is then compared against the quality of clustering results using the DE method.The results show that the AuDE mutation strategy can expand the cluster central search produced by ED so that better clustering quality can be obtained. The comparison of the quality of AuDE and DE using Silhoutte Index is 1:0.816, whereas the use of CS Measure shows a comparison of 0.565:1. The execution time required AuDE shows better but Number significant results, aimed at the comparison of Silhoutte Index usage of 0.99:1 , Whereas on the use of CS Measure obtained the comparison of 0.184:1.
Co-Authors Aina Musdholifah Ari Kusuma Wardana Eka Wahyudi Maulida Ayu Fitriani Tedy Rismawan