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All Journal JURNAL SISTEM INFORMASI BISNIS MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) JIKO (Jurnal Informatika dan Komputer) METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Compiler TELEKONTRAN: Jurnal Ilmiah Telekomunikasi, Kendali dan Elektronika Terapan Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Bakti Cendana: Jurnal Pengabdian Masyarakat Mitra Mahajana: Jurnal Pengabdian Masyarakat Jurnal Dialektika Informatika (Detika) HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi Journal of Informatics Management and Information Technology Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Jurnal Sisfotek Global J-Icon : Jurnal Komputer dan Informatika Jurnal Inovatif Wira Wacana
Sebastianus Adi Santoso Mola
Universitas Nusa Cendana
Agriculture, Biological Sciences & Forestry Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Mechanical Engineering Nursing Public Health Social Sciences

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Journal : JURNAL SISTEM INFORMASI BISNIS

 1 
Integrasi Variable-Centered Intelligent Rule System dengan Teori Dempster-Shafer pada Sistem Pakar Infeksi Saluran Pernafasan Akut Mola, Sebastianus Adi Santoso; Rumlaklak, Nelci D.; Prityaningsih, Ni Putu Dana
JSINBIS (Jurnal Sistem Informasi Bisnis) Vol 9, No 1 (2019): Volume 9 Nomor 1 Tahun 2019
Publisher : Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (370.393 KB) | DOI: 10.21456/vol9iss1pp71-76

Abstract

Acute Respiratory Infection (ARI) is a disease caused by infections of the respiratory tract, larynx, pharynx, sinuses and nose. ARI often causes death because the sufferer who comes for treatment is underestimated is already suffering from severe ARI. In 2013 to 2015 ARI was one of the ten most common illnesses in the city of Kupang, where ARI ranked first, followed by other diseases of the upper respiratory tract and grastitis. This study produced an expert system to diagnose ARI using the Variable Centered-Rule System method which functions to facilitate knowledge development and Dempster-Shafer Theory which serves to overcome uncertainty by entering the density of each symptom of ARI in the system. The VCIRS method is a method of building knowledge and inference strategies on expert systems. This method is rigid in accommodating changes in inference strategies except for changes in knowledge structures. This study aims to make the VCIRS method dynamic in an inference process where the sequence of variables in inference is determined by the occurrence and density of the variable. System accuracy by using medical record data of 95% with the triggering sequence of symptoms becoming dynamic every time a consultation session occurs.
Perbandingan Metode Machine Learning dalam Analisis Sentimen Komentar Pengguna Aplikasi InDriver pada Dataset Tidak Seimbang Mola, Sebastianus Adi Santoso; Luttu, Yufridon Charisma; Rumlaklak, Dessy Nelci
Jurnal Sistem Informasi Bisnis Vol 14, No 3 (2024): Volume 14 Nomor 3 Tahun 2024
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21456/vol14iss3pp247-255

Abstract

The InDriver service is an online transportation service that has more flexibility in price and driver choice by consumers. Various comments from InDriver service users can affect people's views, so it is necessary to carry out a sentiment analysis of these comments. The purpose of this study was to identify positive, negative and neutral sentiments in user comments and to compare the performance of classification methods. The results of analysis with unbalanced datasets show that the Support Vector Machine (SVM) and Logistic Regression methods have the highest accuracy, reaching 89%. However, quality assessment is not only based on accuracy alone. In terms of the balance between precision and recall in the minority (neutral) class, the Random Forest method shows a more balanced performance with an F1-score of 55%. After balancing the dataset with the SMOTE method, performance increases significantly for the Naïve Bayes Classifier method, especially in the neutral class for recall and F1-score metrics of 57% and 52%. In conclusion, SVM and Logistic Regression have high accuracy, but to consider the balance of precision and recall in the minority class, the Random Forest method is recommended.
Co-Authors Abdul G Farid Adelysa P Gitaswara Adriana Fanggidae Adriana Fanggidae Arfan Y Mauko Atok, Yosefa Carela Baun, Diandra Lyan Bethseba Bekayo, Yoseph Kurubingan Boru, Meiton Clarissa Elfira Amos Pah Dwi Prasetyo Elfrida Veranda Beka Dede Emerensiana S.Y Pandie Emerensye S. Y. Pandie Emerensye Sofia Yublina Pandie Febryan Cornelis Lomi Finit, Awi Susanto Giri, Imanuel Raja Gloria, Ayuntha Goru, Alfonsus Maria De Liguori Huan, Arni Yusfin Jakha Rama Phosa Juan Rizky Mannuel Ledoh Juliet Reinita Angelia Lao Kase, Bernardino Antonius Kenlopo, Asnat Nofri Kornelis Letelay Lamapaha, Christian Jaquelino Leobisa, Elisabeth Ester Eunike Luttu, Yufridon Charisma Mbatu, Dinda Permata Meiton Boru Melly, Margaretha Delima N.A.Manbait, Yoseph D’Timor Nelci D Rumlaklak Nomleni, Inggi Rosina Novi Penna Nunes, Ingratcia Oktaviana Polly, Dian Putri Novita Prityaningsih, Ni Putu Dana Rassa, Delfiana M Romy O. D. Djami Roswita R Ligoresi Rumlaklak, Dessy Nelci Rumlaklak, Nelci D. Rumlaklak, Nelcy D. Sain, Adriana Yohana Sani, Michelle Maria Angely Rani Saragih, Alexandro Eurico Yosafat Sihotang, Dony Martinus Sitepu, Rasional Sonlay, Febronia B Ssainah P Faransyah Sultani, Akhzan Akbar Tiwuk Widiastuti Tokan, Diana Inda Carmilla Tungga, Varra Chandrika Kumara Viani Anika Afeanpah Wadu, Benyamin Orison Darling Kana Yelly Y Nabuasa Yelly Yosiana Nabuasa Yelly Yosiana Nabuasa Yublina Pandie, Emerensye Sofia Yulianto Triwahyuadi Polly Yulianto Triwahyuadi Polly