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All Journal Jurnal sistem informasi, Teknologi informasi dan komputer JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI IJISTECH (International Journal Of Information System & Technology) IJISTECH Jurnal Nasional Teknik Elektro dan Teknologi Informasi Journal of Artificial Intelligence and Engineering Applications (JAIEA) Journal of Informatics and Computing
Rakhmawati, Hidayatur
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Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Library & Information Science Mathematics Social Sciences

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Design and Build an ENT Disease Diagnosis Expert System with Bayesian Probability Method Khairina Eka Setyaputri; Hidayatur Rakhmawati
IJISTECH (International Journal of Information System and Technology) Vol 5, No 3 (2021): October
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v5i3.141

Abstract

Sinusitis is one of the most common causes of health problems in the world and the cases are most often found in daily practice. The incidence of sinusitis is a fairly severe disease, namely 1.3 and 3.5 per 100 cases of adults per year to see a doctor. Patients with a disease need accurate information that is easily accessible to find out the disease they are experiencing based on the symptoms they feel. One of the reliable information sourced from experts or people who are experts in their fields and can be easily accessed is the existence of an expert system. In this case, the expert system needed is an ENT (Ear, Nose and Throat) disease diagnosis expert system. The expert system for diagnosing ENT diseases, designed in this study uses the Bayesian Probability method. The research on the design of an expert system for diagnosing ENT diseases that have been carried out can be used as a decision making for ENT patients using the Bayesian Probability method.
Design and Build an ENT Disease Diagnosis Expert System with Bayesian Probability Method Khairina Eka Setyaputri; Hidayatur Rakhmawati
IJISTECH (International Journal of Information System and Technology) Vol 5, No 3 (2021): October
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (732.901 KB) | DOI: 10.30645/ijistech.v5i3.141

Abstract

Sinusitis is one of the most common causes of health problems in the world and the cases are most often found in daily practice. The incidence of sinusitis is a fairly severe disease, namely 1.3 and 3.5 per 100 cases of adults per year to see a doctor. Patients with a disease need accurate information that is easily accessible to find out the disease they are experiencing based on the symptoms they feel. One of the reliable information sourced from experts or people who are experts in their fields and can be easily accessed is the existence of an expert system. In this case, the expert system needed is an ENT (Ear, Nose and Throat) disease diagnosis expert system. The expert system for diagnosing ENT diseases, designed in this study uses the Bayesian Probability method. The research on the design of an expert system for diagnosing ENT diseases that have been carried out can be used as a decision making for ENT patients using the Bayesian Probability method.
IMPLEMENTASI ALAT UKUR TUBUH MANUSIA MENGGUNAKAN SENSOR TANPA SENTUH MLX 90614 BERBASIS ARDUINO Hidayatur Rakhmawati; Mamur Setianama; Ade Irma Setiawan
Journal of Informatics and Computing (RANDOM) Vol. 3 No. 1 (2024): Journal of Informatics and Computing
Publisher : Politeknik Negeri Indramayu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31884/random.v3i1.5

Abstract

The signs that appear due to the covid 19 virus are abnormal human body temperatures (more than 37.5 C). Therefore, it is necessary to build a body temperature measuring device without direct contact to prevent the risk of transmission of the covid 19 virus. This study aims to apply the MLX 90614 sensor to the Arduino-based touchless human body temperature detection system.Extreme programming is suitable where the client is unsure and changes his mind frequently. Extreme programming is suitable for using an iterative approach to developing software. The stages in extreme programming are planning, design, coding and testing. The results of this study can be obtained that measuring body temperature in humans with the MLX 90614 sensor runs smoothly so that it is easier and more practical. The final average of all tests carried out on humans aged 12 years, humans aged 20 years and humans aged 30 years, can produce an average final temperature of MLX 90614 which is 36.77 0C. The final average temperature of the thermogun is 35.32 0C. Then the final average result of the difference between the MLX 90614 sensor temperature test and the thermogun temperature is 1.68. The final average result of the accuracy of the MLX 90614 sensor is 95.52%.
Sistem Pakar Pencegahan Stunting untuk Mendukung Pemahaman Ibu Hamil Terhadap Kebutuhan Asupan Gizi Berbasis Android Hidayatur Rakhmawati; megawati; Nurlaela
Journal of Informatics and Computing (RANDOM) Vol. 3 No. 1 (2024): Journal of Informatics and Computing
Publisher : Politeknik Negeri Indramayu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31884/random.v3i1.57

Abstract

Stunting is a condition of failure to thrive in toddlers due to lack of nutritional intake for a long time and also due to recurrent infections, the causes of these two factors are influenced by inadequate parenting patterns. Thus, an expert system for stunting prevention is needed that will help provide insight for pregnant women about their nutritional needs and make it easier for parents to consult about stunting prevention and get reminders about nutritional needs during pregnancy. An Android-based stunting prevention expert system to support pregnant women's understanding of nutritional intake needs was built using the forward chaining method and research methods in the form of experimental methods aimed at providing logical information regarding stunting prevention regarding nutritional intake needs. The results of the research show that the forward chaining method is suitable for use, including in the aspect of conclusion results based on questionnaires that have been filled out appropriately and satisfactorily with a percentage of 80%, the information provided is appropriate, resulting in a percentage of 88%, 91% stated that this system helps pregnant women in preventing stunting. , as well as producing conclusions regarding the application, namely the application is easy to use with a percentage of 85%, menu navigation is easy to use with a percentage of 88%, the application display can be seen comfortably with a percentage of 88%.
PENERAPAN ALGORITMA NAIVE BAYES UNTUK MENDIAGNOSIS DAN K-MEANS UNTUK MENGELOMPOKKAN PENYAKIT GAGAL JANTUNG Rakhmawati, Hidayatur; Aeni, Arinda Nur
Jurnal Sistem Informasi, Teknologi Informatika dan Komputer Volume 15 No 1, September Tahun 2024
Publisher : Universitas Muhammadiyah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24853/justit.15.1.273-283

Abstract

Penyakit gagal jantung menjadi salah satu penyakit yang memiliki angka kematiian yang tinggi. Penyebab dari gagal jantung antara lain disfungsi miokard, endokard, perikadium, pembuluh darah besar, dan kelainan katup. Terjadinya kesalahan dalam mendiagnosis pasien menjadi permasalahan dalam penelitian ini. Adanya kemajuan teknologi dapat memberi kemudahan dalam segala aktivitas salah satunya dibidang kesehatan. Penelitian ini bertujuan untuk menerapkan algoritma naive bayes classifier untuk mendiagnosis benar tidaknya seorang pasien terkena gagal jantung dan k-means untuk mengelompokkan hasil dari diagnosis penyakit gagal jantung. Metode data mining yang digunakan pada penelitian ini adalah algoritma Naive Bayes untuk mendiagnosis dan algoritma K-Means untuk mengelompokkan penyakit gagal jantung. Data yang digunakan pada penelitian ini bersumber dari Rumah Sakit Umum Muhammadiyah Siti Aminah Bumiayu dengan jumlah 216 record dan 10 atribut. Hasil penelitian menunjukkan bahwa kinerja algoritma naive bayes menggunakan pengujian akurasi confusion matrix dengan nilai akurasi sebesar 80,22%. Berdasarkan hasil pengujian k-means diperoleh sebanyak 94 record yang terkena penyakit gagal jantung dari 216 record. 94 record tersebut diolah kembali menggunakan algoritma k-means menjadi 3 cluster, yaitu cluster rendah, sedang tinggi. Hasil dari klasterisasi yaitu cluster 0=27 record, cluster 1=50 record, cluster 2=17 record dengan mendapatkan validasi davies bouldin index (DBI) sebesar 1,450. Kesimpulannya yaitu penerapan algoritma naive bayes dan k-means dapat digunakan untuk diagnosis terkena penyakit gagal jantung dan mengklaster penyakit gagal jantung
Optimasi Algoritma K-Nearest Neighbors Berdasarkan Perbandingan Analisis Outlier (Berbasis Jarak, Kepadatan, LOF) Fitri Ayuning Tyas; Mahda Nurayuni; Hidayatur Rakhmawati
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 13 No 2: Mei 2024
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v13i2.9579

Abstract

The current data growth affects data analysis in various fields, such as astronomy, business, medicine, education, and finance. The collected and stored data contain extreme values or observation values different from most other observation value results. These extreme values are called outliers. Outliers on some data often hold valuable information, necessitating thorough examination to determine whether to retain or discard them prior to data mining application. Outlier detection can be performed as a part of data preprocessing using outlier analysis techniques. Commonly utilized outlier analysis techniques encompass distance-based methods, density-based methods, and the local outlier factor (LOF) method. k-nearest neighbors (KNN) are a data mining algorithm susceptible to outliers due to its reliance on the value of k. Hence, having an appropriate handling mechanism is essential when employing KNN on datasets that contain outliers. The experimental method was selected to apply the proposed approach, aiming to optimize the KNN algorithm through a comparison of outlier analysis methods (KNN-distance, KNN-density, and KNN-LOF). The results revealed that KNN-density outperformed the others significantly: achieving an average accuracy of 99.34% at k=3 and k=5 for Wisconsin Breast Cancer, 85.25% at k=7 for Glass, and 85.45% at k=5 for Lymphography. Moreover, both the Friedman and Nemenyi tests validate a notable distinction between KNN-density and KNN-LOF.
PERBANDINGAN ALGORITMA NAIVE BAYES DAN C.45 DALAM DIAGNOSIS PENYAKIT PARU-PARU Rakhmawati, Hidayatur; Sindyka, Afifah
Jurnal Sistem Informasi, Teknologi Informatika dan Komputer Vol 15 No 3 (2025): Volume 15 No 3, Mei Tahun 2025
Publisher : Universitas Muhammadiyah Jakarta

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

Abstract

Penyakit paru-paru sangat berbahaya karena dapat menyebabkan kerusakan pernafasan dalam jangka pendek maupun panjang. Masalah yang sering terjadi misalnya terkena penyakit paru-paru karena kualitas udara yang tercemar/terkontaminasi, sehingga udara yang dihirup masyarakat banyak mengandung virus/bakteri. Timbulnya kesalahan diagnosis pada pasien menjadi permasalahan dalam penelitian ini. Adanya teknologi dapat memudahkan segala aktivitas, termasuk dalam bidang kesehatan. Penelitian ini bertujuan untuk mengetahui kinerja algoritma Naive Bayes dan C4.5 sebagai perhitungan hasil diagnostik untuk mengklasifikasikan orang yang terkena penyakit paru-paru. Metode ekstraksi data yang digunakan dalam penelitian ini adalah algoritma Naive Bayes dan C4.5 untuk mengklasifikasikan hasil diagnosa penyakit paru-paru. Klasifikasi naif pada algoritma Bayes dilakukan dengan mentransformasikan kumpulan data yang menentukan frekuensi setiap nilai kelas dan metode klasifikasi pada algoritma C4. 5 dengan mentransformasikan fakta menjadi pohon keputusan sesuai aturan yang ada. Data yang digunakan dipenerapan kedua algoritma ini dari dataset pribadi yang terdiri dari total 325 record dengan 5 atribut. Hasil penelitian menunjukkan bahwa pengukuran kinerja algoritma Naive Bayes menggunakan matriks konfusi dengan validasi silang 10 kali lipat menghasilkan nilai akurasi sebesar 91,08%. Pengukuran kinerja yang sama juga dilakukan pada algoritma C4. 5 masing-masing menggunakan matriks konfusi dan validasi silang 10 kali lipat, menghasilkan nilai akurasi sebesar 89,23%. Hasil akurasi menunjukkan Algoritma Naive Bayes lebih unggul dibandingkan C4. 5 dengan selisih 1,85%. Kesimpulannya adalah algoritma Naive Bayes lebih baik dalam mengklasifikasikan hasil diagnosa penyakit paru.
Komparasi Perhitungan Jarak K-Means Dalam Pengklasteran Tingkat Pengangguran Terbuka di Indonesia Ghoni, Umar; Hidayat, Nur Wahyu; Rakhmawati, Hidayatur; Lestari, Yuniarti
JATISI Vol 12 No 4 (2025): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v12i4.13750

Abstract

Abstract Unemployment is a short-term economic problem that significantly affects social conditions and public welfare. The Open Unemployment Rate (OUR) serves as an important indicator to measure unemployment levels. In Indonesia, OUR data are periodically published by the Central Bureau of Statistics (Badan Pusat Statistik, BPS) by province and year. Analyzing these data helps identify regions with high or low unemployment rates to support effective labor policy formulation. This study applies the K-Means clustering algorithm to classify Indonesia’s open unemployment rate using four distance metrics: Euclidean, Manhattan, Chebyshev, and Cosine Similarity. The clustering performance was evaluated using the Davies-Bouldin Index (DBI) to determine the best distance metric. The results indicate that Chebyshev Distance produced the best cluster quality with a DBI value of 0.606, while Cosine Similarity yielded the poorest result with a DBI of 1.445. Therefore, Chebyshev Distance is recommended as the most suitable distance metric for clustering Indonesia’s open unemployment rate using the K-Means algorithm. Keywords: open unemployment, K-Means, distance metric, Davies-Bouldin Index.
Website-Based School Financial Information System Takhrisna Amila Alfaida; Utami, Syefti Rahma; Febikhanaya Putri; Hidayatur Rakhmawati; Alma Fatikhul Khak; Muhammad Dafie Ardiansyah
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1925

Abstract

The rapid development of information technology has brought significant changes in data and information management in the educational environment. This research focuses on the development of a website-based Financial Information System tailored to the needs of SDIT Binaul Izzah Bumiayu. This system was designed using the waterfall method with stages of needs analysis, design, implementation, and testing to improve efficiency, accuracy, and ease in managing school financial data which was previously still manual. The results of the study indicate that the system created is able to assist schools in the process of recording, recapitulating financial reports, and accelerating data access while reducing human error. Recommendations for future system development include the addition of payment notification features, automatic reports, student data integration, improvement of supporting facilities, and ongoing cooperation between schools and universities, followed by periodic evaluations to improve system quality.
Co-Authors Ade Irma Setiawan Aeni, Arinda Nur Alma Fatikhul Khak Febikhanaya Putri Ghoni, Umar Khairina Eka Setyaputri Lestari, Yuniarti Mahda Nurayuni Mamur Setianama Megawati Muhammad Dafie Ardiansyah Nur Hidayat, Wahyu Nurlaela Sindyka, Afifah Takhrisna Amila Alfaida Utami, Syefti Rahma