Case Based Reasoning Untuk Mendiagnosa Penyakit Kehamilan Menggunakan Cosine Similarity

The application of case-based reasoning in diagnosing pregnancy deseases is motivated by the lack of number of obstetricians. The use of CBR aims to solve new problems by adapting the solutions contained in the previous case, by calculating the level of similarity. Calculation of similarity value using cosine similarity method, with threshold equal to 100%. This system can diagnose 6 diseases, 28 existing symptoms. System outbreaks of illness experienced by patients based on symptoms induced by non-specialist medical personnel, as well as handling solutions accompanied by a presentation of similarities with previous cases to indicate the degree of truth of possible diagnosis. Based on the results of case testing, the results obtained: the system can retrieve the exact old case and have used the cosine similarity methodology correctly, shown with 100% accuracy results, and using 104 cases is optimal enough to diagnose 6 illnesses shown with average results Similarity to 20 cases is 90%

Sistem pakar mendiagnosa penyakit pada balita usia 0 – 60 bulan menggunakan metode Dempster-Shafer

Toddlers are susceptible to germs and viruses so that they are susceptible to various types of diseases because the immune system that has not been built properly. Most parents don't know about the symptoms and diseases suffered by toddlers, which is why sometimes the disease gets worse. The solution to the disease experienced by toddlers can be overcome is to take them to a hospital or health center. Binaus Health Center located in Mollo Tengah District, TTS Regency is one of the community health centers where most of the patients are toddlers. The obstacle of Binaus Community Health Center in operating is the specialist doctor of children or toddlers is not available, so an expert system was created as a media for consultation and monitoring of toddlers with the Dempster-Shafer Method whose end result was the diagnosis of diseases suffered by toddlers. Comparison of expert diagnosis results with expert systems obtained an average range of trust of 92.86% for appropriate testing experts above the threshold value and testing below the threshold value obtained miss-classification 7.14%. The expert system does not provide 100% results according to expert diagnosis not because of inference errors but the inappropriate value of expert density. Data testing based on case studies taken from medical record data at the Binaus Health Center, Kec. Mollo Tengah, Kab. TTS has a 100% accuracy rate.    Balita rentan terhadap kuman dan virus sehingga mudah terkena berbagai jenis penyakit karena sistem imun yang belum terbangun dengan sempurna. Kebanyakan orangtua kurang tahu tentang gejala dan penyakit yang diderita oleh balita, karena itulah terkadang penyakitnya semakin parah. Solusi supaya penyakit yang dialami oleh balita bisa teratasi adalah dengan membawanya ke rumah sakit atau pusat kesehatan. Puskesmas Binaus yang terletak di Kecamatan Mollo Tengah, Kabupaten TTS merupakan salah satu pusat kesehatan masyarakat yang kebanyakan pasiennya adalah balita. Kendala Puskesmas Binaus dalam beroperasi adalah dokter spesialias anak atau balita tidak tersedia, sehingga dibuatlah sebuah sistem pakar sebagai media konsultasi dan monitoring terhadap balita dengan Metode Dempster-Shafer yang hasil akhirnya berupa diagnosa penyakit yang di derita oleh balita. Perbandingan hasil diagnosis pakar dengan sistem pakar diperoleh rata-rata rentangan kepercayaan sebesar 92,86% untuk pengujian yang sesuai pakar berada diatas nilai treshold dan pengujian yang dibawah nilai treshold diperoleh miss-klasifikasi 7,14%. Sistem pakar tidak memberikan hasil 100% sesuai dengan diagnosis pakar bukan karena kesalahan pada inferensi tetapi pemberian nilai densitas pakar yang kurang tepat. Pengujian data berdasarkan studi kasus yang diambil dari data rekam medis pada Puskesmas Binaus Kec. Mollo Tengah, Kab. TTS memiliki tingkat akurasi 100%. &nbsp

PENALARAN BERBASIS KASUS UNTUK MENDIAGNOSA PENYAKIT INFEKSI MENULAR SEKSUAL (IMS) MENGGUNAKAN ALGORITMA WEIGHTED EUCLIDEAN DISTANCE

Case-based reasoning is a methodology for solving problems by utilizing previous experience. In this study the authors apply case-based reasoning to diagnose sexually transmitted infection using the weighted Euclidean distance method. Source of the knowledge base was obtained by collecting medical record of patients with sexually transmitted infections in 2016-2017. The process of finding a solution starts with eliminating irrelevant data using the C4.5 method and continues with the calculation of the similarity value using the Weighted Euclidean Distance algorithm. This system can diagnose 5 types of sexually transmitted infections based on 123 existing symptoms. System result in the form of sexually transmitted infections based on symptoms experienced by the patient, treatment solution and presentation of similarities between new cases and old cases. Based on the result of testing with 127 cases of sexually transmitted infections obtained result: testing uses the K-Fold Cross Validation scenario, the total data is divided into 10fold and the testing process is divided into 2 parts, namely testing using indexing and testing without using indexing. For testing using the highest accuracy indexing obtained at 90.84% in the second fold, and the average accuracy of the entire fold is 88.55% with the average time generated 9498 ms (millisecond), while testing without using the highest accuracy indexing obtained by 63.03% in the second fold, and the average accuracy of the entire fold is 53.48% with the average time generated 9975 ms (millisecond).  Penalaran Berbasis Kasus adalah sebuah metedologi untuk penyelesaian masalah dengan memanfaatkan pengalaman sebelumnya. Pada penelitian ini penulis menerapkan penalaran berbasis kasus untuk mendiagnosa penyakit infeksi menular seksual menggunakan metode weighted euclidean distance. Sumber basis pengetahuan diperoleh dengan mengumpulkan berkas rekam medis pasien penyakit infeksi menular seksual pada tahun 2016-2017. Proses pencarian solusi dimulai dengan mengeliminasi data yang tidak relevan menggunakan C4.5 dan berlanjut dengan perhitungan nilai kemiripan menggunakan algoritma weighted euclidean distance. Sistem ini dapat mendiagnosis 5 jenis penyakit infeksi menular seksual berdasarkan 123 gejala yang ada. Hasil sistem berupa jenis penyakit infeksi menular seksual berdasarkan gejala yang dialami pasien, solusi pengobatan dan presentasi kemiripan kasus baru dengan kasus lama. Berdasarkan hasil pengujian dengan 127 kasus infeksi menular seksual (IMS) didapatkan hasil: Pengujian menggunakan skenario K-Fold Cross Validation, total data dibagi menjadi 10 fold dan proses pengujian dibagi menjadi 2 bagian yaitu pengujian menggunakan indexing dan pengujian tanpa menggunakan indexing. Untuk pengujian menggunakan indexing akurasi tertinggi yang didapat sebesar 90.84% pada fold ke-2, dan rata-rata akurasi dari keseluruhan fold adalah sebesar 88.55% dengan rata-rata waktu yang dihasillkan 9498 ms (milidetik) sedangkan pengujian tanpa menggunakan indexing akurasi tertinggi yang didapat sebesar 63.03% pada fold ke-2, dan rata-rata akurasi dari keseluruhan fold adalah sebesar 53.48% dengan rata-rata waktu yang dihasilkan 9975 ms (milidetik). &nbsp

Implementasi Case Base Reasoning Menggunakan Metode Cosine Similarity Untuk Mendiagnosa Penyakit Pada Sapi

Case Based Reasoning (CBR) is a case-breaking technique based on experience in cases that have previously occurred with the highest similarity value. In this study, the authors apply CBR to diagnose cow disease. Sources of system knowledge are obtained by collecting cases from medical records on 2014, 2016, and 2017. The system uses the Rough Set method for indexing and the calculation of similarity values ​​using the Cosine Similarity method with threshold 70%. This system is able to diagnose 15 diseases based on 29 existing symptoms. The output of the system in the form of the illness experienced, the solution and the presentation of similarities with the previous case to show the truth level of possible diagnose. Based on the test of 30 cases on casebase obtained system accuracy at second part is 27% and at third part the system gets the best result using 3 fold by 33,33%. The system produces low accuracy due to the small number of cases and the scattered data in the case. &nbsp

Implementasi Penetapan Pajak Kendaraan Bermotor Ubah Bentuk pada Samsat Kabupaten Timor Tengah Selatan

The problem in doing the majoring in SAMSAT of sub-province of Timor Tengah Selatan can be solved by using information system. In a month SAMSAT of sub-province of Timor Tengah have served 3.131 taxpayer, that means for a day SAMSAT of sub-province of Timor Tengah served approximately reach 150 taxpayer and total vehicles in registration is 39.492 vehicles. The system that is developed to maintain vehicles data, types of vehicle data, vehicles brands data, dumps data, vehicles price data, registration data, and to count vehicles tax determining rightly dan quickly and can gain the report of all registration data, specific report of the vehicles, specific report of vehicles tax determining and receipt tax payments of vehicles transform. This system capable to answered the hypothesis H0 about contentment of service with satisfaction level more than 70% viz 78%.     &nbsp

Penerapan Modified Certainty Factor dalam Sistem Pakar Tes Kepribadian Flag

Expert system is one of artificial intelligence engines that is using specific knowledge of an expert to solve a specific problem. In this study, the expert system is built to implement FLAG personality test using Modified Certainty Factor method in order to help counselee knowing his personality type and the careers suitable for him. Knowledge source for this system is obtained from the book Tes Bakat Anda (Test Your Own Aptitude) by Jim Barrett and Geoff Williams (2002) along with several consultations with Irianti Agustina, S.Pd., M.Pd. and Dra. Sri Rahayu Djami. This system is able to provide the output in the form of personality type of the counselee as well as career recommendations suitable for him. Based on study on 141 data of counselees, the results are: By using Modified Certainty Factor, this expert system has accuracy of 83.69%, and provides more certain output than the output provided by the conventional FLAG. Therefore, researcher recommends the using of Modified Certainty Factor method to improve any other personality test which still has not given certain output

Penerapan Metode Fuzzy Service Quality (Servqual) untuk Menganalisa Kepuasan Pelayanan Pendidikan pada Jurusan Ilmu Komputer Fakultas Sains dan Teknik Universitas Nusa Cendana

In carrying the service of education, Departmen Of Computer Science Faculty Of Science And Technique University Of Nusa Cendana, trying to give the service that can be contented the students. So far the departmen doesn't know how the assessment of students against the service given. The survey of students statisfaction can be a manner to deliver what they feel and what is the hope of students against the service of education. Fuzzy service quality (servqual) method can be used to analyze the statisfaction of service. The concept of fuzzy is used to help the respondent for giving value that more objective, while the servqual method define the statisfaction of service as how far the difference between the facts and the hope on the service that is received by respondent. This method have five dimention that are tangibles, reliability, responsiveness, assurance dan emphaty. The result of service statisfaction analysis in Computer Science Department using the fuzzy method in the academic year 2016/2017 the value is GAP -14.3197, that means the giving service is not statisfy. Based on the result of analysis gived repair recommendation of each dimention that is the value of GAP is smallest negative

NAZIEF-ADRIANI STEMMER DENGAN IMBUHAN TAK BAKU PADA NORMALISASI BAHASA PERCAKAPAN DI MEDIA SOSIAL

The use of non-standard language is increasingly prevalent in communication on social media. The use of indefinite language is not limited to sentences, clauses, or phrases but also word usage. In this study, the nonstandard word (NSW) will be normalized to the Indonesian standard word (SW). The Nazief-Adriani stemmer (NAS) method was developed into a nonstandard stemmer (NSS) by increasing its ability to detect non-standard additives. The Needleman-Wunsch similarity algorithm is used to weight the matches. The test results with the Mean Reciprocal Rank (MRR) of 3,438 NSW found that the use of NSS with the number of queries = 9 (Q = 9) had the highest of 79.26% with an average of 50.48%. Meanwhile, MRR testing using NAS with Q = 9 got the highest result of 72.87% and an average of 47.23%. Of the two MRR tests carried out, there were 3 letters that had the highest stemming results, both in tests using NAS and using NSS, namely the initial letters r, f and j. The most significant increase in MRR value occurs in the initial letters 'd', 'n' and 't' which are the initial letters of some non-standard affixes.Penggunaan bahasa tak baku semakin marak dalam komunikasi di media sosial. Penggunaan bahasa tak baku tidak terbatas pada kalimat, klausa, atau frasa saja namun juga pada penggunaan kata. Pada penelitian ini, akan dilakukan normalisasi kata yang tak baku/ nonstandard word (NSW) tersebut ke kata baku/ standard word (SW) Bahasa Indonesia. Metode stemmer Nazief-Adriani (Nazief-Adriani stemmer (NAS)) dikembangkan menjadi nonstandard stemmer (NSS) dengan meningkatkan kemampuannya untuk mendeteksi imbuhan tak baku. Tujuan penelitian ini adalah membandingkan penggunaan NAS dan NSS dalam normalisasi NSW.  Algoritma kemiripan Needleman-Wunsch digunakan untuk membobot hasil pencocokan. Hasil pengujian dengan Mean Reciprocal Rank (MRR) pada sebanyak 3.438 NSW didapatkan penggunaan NSS dengan jumlah kueri = 9 (Q=9) memiliki tertinggi sebesar 79.26% dengan rata-rata sebesar 50.48%. Sedangkan pengujian MRR menggunakan NAS dengan Q=9 mendapatkan hasil tertinggi sebesar 72.87% dan rata-rata sebesar 47.23%. Dari dua pengujian MRR yang dilakukan, ada 3 huruf yang memiliki hasil stemming tertinggi, baik dalam pengujian menggunakan NAS maupun menggunakan NSS yaitu huruf  awal r, f dan j. Peningkatan nilai MRR paling signifikan terjadi pada huruf awal ‘d’, ‘n’  dan  ‘t’ yang merupakan huruf awal dari sebagian imbuhan tak standar