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All Journal Journal of Mathematical and Fundamental Sciences IJoICT (International Journal on Information and Communication Technology) JURNAL ILMIAH MATEMATIKA DAN TERAPAN BAREKENG: Jurnal Ilmu Matematika dan Terapan Communication in Biomathematical Sciences Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Jurnal Pengabdian Farmasi dan Sains
Juni Wijayanti Puspita
Tadulako University
Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Economics, Econometrics & Finance Energy Engineering Immunology & microbiology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Social Sciences Transportation

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Application of The C4.5 Algorithm to Get Customer Satisfaction Levels (Case Study : Toko Craft Palu, Jl. Setia Budi) Ningrum, Desy Riani Sukma; Resnawati; Najar, Abdul Mahatir; Puspita, Juni Wijayanti
JURNAL ILMIAH MATEMATIKA DAN TERAPAN Vol. 21 No. 1 (2024)
Publisher : Program Studi Matematika, Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/2540766X.2024.v21.i1.16958

Abstract

Customer satisfaction refers to the response expressed by customers as a result of their evaluation of the perceived difference between their initial expectations before purchase and the performance of the service after purchase. Several specific factors impact the purchasing process and the performance of the product service, such as uncertainty in store operating hours and limited availability of inventory. These related issues have an impact on customer satisfaction, especially at Craft Palu store. The aim of this research is to determine the level of customer satisfaction and accuracy level using the decision tree method, specifically the C4.5 Algorithm. In this study, the measured variables of customer satisfaction at Craft Palu store are Tangibles, Reliability, Responsiveness, Assurance, and Empathy. Based on the results of this research, it is found that Reliability is the most influential variable with an index value is 80,6% of respondents satisfied with the 5th statement, and accuracy test results using the C4.5 Algorithm in python software show an improvement with a decent final accuracy is 90%. Therefore, the C4.5 Algortihm is suitable for measuring customer satisfaction.
Analisis Kestabilan Lokal pada Model SEIR Patogenesis Frambusia dengan Infeksi Primer-Sekunder dan Tersier Lasongke, Fahri Alam; Puspita, Juni Wijayanti; Ratianingsih, Rina; Utami, Vicya; Salman, Salman
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 12 Issue 2 December 2024
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v12i2.27847

Abstract

Yaws is a skin disease characterized by red spots that can worsen if not treated promptly. This disease is caused by the bacteria Treponema Pallidum Pertenue. The symptoms of yaws have five stages, namely the primary stage, primary to secondary latent stage, secondary stage, secondary to tertiary latent stage, and tertiary stage. A mathematical model is one solution to describe the prognosis of yaws disease. Here, the population was divided into 5 sub-populations, namely susceptible sub-population, exposed sub-population, infected sub-population in the primary and secondary stages, infected sub-population in the tertiary stage, and recovered sub-population. The mathematical model of the spread of yaws disease is written as a system of nonlinear differential equations whose stability is analyzed around the critical point. From the system of differential equations, two critical points are obtained which describe the disease-free condition and the endemic condition. In this study, the existence and stability of both critical points can be guaranteed. Furthermore, numerical simulations were conducted using yaws disease data in Indonesia. Simulation results show that the transmission of yaws disease in Indonesia can be controlled by reducing contact between the primary-secondary infected population and the susceptible population.
MODEL DINAMIKA TRANSMISI PENYAKIT SCHISTOSOMIASIS Resnawati, Resnawati; Hajar, Hajar; Puspita, Juni Wijayanti
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 15 No 3 (2021): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (561.199 KB) | DOI: 10.30598/barekengvol15iss3pp503-512

Abstract

Schistosomiasis merupakan penyakit endemik yang disebabkan oleh cacing trematoda bergenus Schistosoma dengan hospes perantara keong bergenus Oncomelania. Di Indonesia, penyakit ini hanya ditemukan di dataran tinggi Lindu, Napu, dan Bada, Kabupaten Sigi dan Poso, Provinsi Sulawesi Tengah. Dalam penelitian ini, akan dikonstruksi model transmisi penyakit Schistosomiasis yang melibatkan populasi manusia, cacing Schistosoma japonicum, dan keong Oncomelania hupensis lindoensis yang merupakan keong endemik di Indonesia. Dari model tersebut diperoleh titik ekuilibrium bebas penyakit Schistosomiasis dengan dan tanpa kehadiran populasi keong serta titik ekuilibrium endemik. Hasil kajian terhadap perilaku solusi mengindikasikan bahwa penyakit Schistosomiasis akan menghilang dari daerah endemik di masa yang akan datang, dengan tetap mempertahankan keberadaan populasi keong, jika dapat meminimalisir peluang kontak sukses terinfeksi Schistosomiasis yang termuat dalam syarat kestabilan solusi. Simulasi numerik diberikan untuk mendukung hasil tersebut.
Web-based Application for Diagnosis of Diabetes using Learning Vector Quantization (LVQ) Puspita, Juni Wijayanti; Yanto, Kevin Jieventius; Pettalolo, Andi Moh. Ridho; Dg. Matona, Moh. Ali Akbar; Lilies, Handayani
International Journal on Information and Communication Technology (IJoICT) Vol. 10 No. 1 (2024): Vol. 10 No.1 June 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v10i1.941

Abstract

Diabetes is a chronic disease that causes the most deaths in the world. This disease can cause long-term complications that develop gradually, such as heart attacks, strokes, and problems with the kidneys, eyes, skin, and blood vessels. Therefore, early diagnosis of diabetes is crucial for patients to know their diabetes status. In this study, we designed a web-based application for diabetes diagnosis using Learning Vector Quantization (LVQ). The dataset was collected from Kaggle's Diabetes Dataset which contains eight attributes, namely pregnancy, glucose, blood pressure, insulin, skin thickness, BMI, diabetes lineage function, and age, with two classes, namely negative diabetes (healthy) and positive diabetes. The results show that the best accuracy is 73.1% with a learning rate of 0.001. These findings can help patients detect diabetes problems early.
Co-Authors Aan Bin Mardi Abdul Mahatir Najar Agus Indra Jaya Ahmad Rizal Dg. Matona, Moh. Ali Akbar Edy Soewono Edy Soewono Fadjryani, Fadjryani Fakhruddin, Muhammad Fatkhur Rohim Govan, Govan Hajar Hajar Hilda Fahlena Ismawati, Nurul Jafaruddin Hamid Kenden, A N Lasongke, Fahri Alam Lilies, Handayani Maulidyani Abu Muhammad Fakhruddin Ningrum, Desy Riani Sukma Nuning Nuraini Nurul Ismawati Pettalolo, Andi Moh. Ridho Ratianigsih, Rina Resnawati Rina Ratianingsih Rohim, Fatkhur Rovina Ruslami, Rovina Salman Salman Sapto Wahyu Indratno See Siew Ju Suryani Gunadharma sutimin sutimin Tri Hanggono Achmad Ulan Rahmawati Utami, Vicya Yanto, Kevin Jieventius