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J I M P - Jurnal Informatika Merdeka Pasuruan
Published by Universitas Merdeka Pasuruan
ISSN : 25025716     EISSN : 25031945     DOI : -
Core Subject : Science,
Computer Science & IT
Jurnal Informatika Merdeka Pasuruan (JIMP) terbit 3 kali dalam satu tahun yaitu dibulan maret, agustus dan desember. Memuat tulisan ilmiah yang berhubungan dengan bidang teknologi informasi serta aplikasi teknik informatika. Jurnal JIMP terbitan berkala ini adalah hasil penelitian dari tugas akhir penelitian dari dalam dan luar Departemen Fakultas Teknologi Informasi Universitas Merdeka Pasuruan.
Arjuna Subject : -
Articles 5 Documents
 1 
Search results for , issue "Vol 6, No 3 (2021): DESEMBER" : 5 Documents clear
Sistem Pakar untuk Mendiagnosis Gangguan Tidur Menggunakan Metode Dempster Shafer Ivo Dwi Ananda; Rahmad Kurniawan; Novi Yanti; Fitri Insani
J I M P - Jurnal Informatika Merdeka Pasuruan Vol 6, No 3 (2021): DESEMBER
Publisher : Fakultas Teknologi Informasi Universitas Merdeka Pasuruan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37438/jimp.v6i3.354

Abstract

Poor quality of sleep can cause psychological and physiological health problems. Estimated from 238,452 million people in Indonesia every year, about 67% elderly people reported having trouble sleeping. With a prevalence of 10% or about 28 million people suffering from sleep disorders. This makes Indonesia has the highest number of sleep disorders in Asia. The cases of sleep disorders increased during the Covid-19 pandemic by 23.87% in general public and by 36.53%  in medical personnel. This study aims to build a system that can diagnose sleep disorders like an expert. This study employed the Dempster Shafer method with 25 symptoms and four types of sleep disorders. The Dempster Shafer method is a commonly applied technique which is combining evidence in uncertainty cases. The experimental testing based on the validation of the results of the system diagnosis with expert diagnosis, the percentage of test accuracy is 90%. It can be concluded that the system potentially be used for early sleep disorder diagnosis.Keywords—expert system, dempster shafer, sleep disorders, sleep quality, uncertainty.
Pengelompokkan Judul Buku dengan Menggunakan Algoritma K-Nearest Neighbor (K-NN) dan Term Frequency – Inverse Document Frequency (TF-IDF) Fahrur Rozi; Farid Sukmana; Muhammad Nabil Adani
J I M P - Jurnal Informatika Merdeka Pasuruan Vol 6, No 3 (2021): DESEMBER
Publisher : Fakultas Teknologi Informasi Universitas Merdeka Pasuruan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37438/jimp.v6i3.346

Abstract

Universitas Bhinneka PGRI Library has many collections in both printed and digital forms, which collections will increase over time. Thus the number of collections of books in the library will be more and more diverse, it will make the process of grouping existing collections difficult. The method used in this study is data mining with the K-Nearest Neighbors (K-NN) algorithm approach by combining TF-IDF as word frequency weighting. The stages of working on the K-NN method in this study went through 4 stages, namely: (1) text preprocessing by applying the tokenization method, case folding, stopword removal and stemming, (2) Word weighting using the TF-IDF method (3). Modeling the k value from a minimum limit of 1 to a maximum limit of 30. (4) Classification of data using the most optimal k value based on k value modeling. (5) discussion of classification results. Data collection techniques using literature studies and datasets. With this classification system, it is expected to provide useful information for users. In addition, this study also aims to implement the K-NN method by combining it with TF-IDF while at the same time knowing the accuracy of the sales prediction system. The results of this study are based on the highest accuracy value for the classification of book titles of 66.67% and the lowest accuracy value of 60% with an average accuracy value of 63.33%.Kata kunci— Data Mining, K-Nearest Neighbor (K-NN), TF-IDF
Prototipe Monitoring Daya Listrik dan Pengendalian Perangkat Elektronik Skala Industri Berbasis IoT di CV. Wellracom Nusantara Surabaya Samsul Huda; Trio Bekti Imansah; Elvianto Dwi Hartono
J I M P - Jurnal Informatika Merdeka Pasuruan Vol 6, No 3 (2021): DESEMBER
Publisher : Fakultas Teknologi Informasi Universitas Merdeka Pasuruan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37438/jimp.v6i3.340

Abstract

The electricity bill accounts for a considerable amount of the full operating costs of industries. CV. Wellracom Nusantara has a lot of electronic equipment and industrial machines for production. Here, the company does not really know the electricity usage each month. They only know the significant amount of bill payments and over-budget when the electricity bill comes. Therefore, it is crucial for companies to accurately estimate future electricity costs as a strategy to reduce over-budget and uncontrolled costs. To overcome the problems, we propose a prototype monitoring system to make it more comfortable to monitor the use of electrical power in the company. This solution allows the electricity consumption from all devices to be monitored and controlled. The prototype monitors the use of electrical energy from each device and controls the electronic devices. The proposed solution adopts IoT technology using industrial-scale devices. This can monitor electricity consumption data from each device in real-time, record historical data from daily to monthly, send notifications, and control on/off devices. This prototype has an accuracy of 98% of the reading measurement results compared to the digital AVOmeter. Through a simple experiment using electric power loads of two light bulbs and two laptop chargers for 24 hours, we confirmed that the implemented prototype runs correctly.Keywords— electricity usage management, electricity bill, IIoT, IoT
PENGUKURAN TINGKAT KEMATANGAN KOPI ARABIKA MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBOR Anastasia L Maukar; fitri marisa; Ahmad Farhan; Erdian Ari Widodo; Ilhamsyah I; Inayati Sa'adah; Rivaldo Tito L Dasilva
J I M P - Jurnal Informatika Merdeka Pasuruan Vol 6, No 3 (2021): DESEMBER
Publisher : Fakultas Teknologi Informasi Universitas Merdeka Pasuruan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37438/jimp.v6i3.280

Abstract

Coffee has an important role in improving the national economy. Coffee is also one of the fourth major export commodities in foreign countries. Assessing the level of ripeness of a good coffee can be seen depending on the type of coffee itself. Arabika coffee will start to ripen on days 310 to 350 and for Arabica coffee types it will start to look ripe at the age of 210 to 250 days. In classifying coffee maturity, the K-Nearest Neighbor (KNN) method can be used. By taking a sample image of 3 arabika coffee grains with different levels of maturity twice. The existing data will be processed using the HSV feature to assess the RGB of the coffee bean image data. Based on the test results that have been determined. An accuracy calculation has been used to measure KNN and HSV's performance in determining the ripeness of arabika coffee. The calculation results show the performance of KNN at K=1 is outstanding,, 93.33%.Keywords— Arabica Coffee, ripeness level, K-Nearest Neighbor, HSV, accuracy
Penerapan Algoritma K-Means Clustering dan Correlation Matrix Untuk Menganalisis Risiko Penyebaran Demam Berdarah di Kota Pekanbaru m azwan; Rahmad Kurniawan; Pizaini Pizaini; Fitri Insani
J I M P - Jurnal Informatika Merdeka Pasuruan Vol 6, No 3 (2021): DESEMBER
Publisher : Fakultas Teknologi Informasi Universitas Merdeka Pasuruan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37438/jimp.v6i3.353

Abstract

Dengue cases in Pekanbaru in November 2020 reached 2,788 cases and 33 deaths. The government has carried out socialization to eradicate mosquito nests and provided vector control tools and materials. However, the government's efforts were not practical because the applied method has not been able to refer to vector data and information. Machine learning can be used to analyze specific problems such as Dengue. Therefore this study employed a Machine Learning algorithm, i.e., k-means clustering and correlation matrix for dengue risk analysis in Pekanbaru. This study obtained 12 sub-districts and 50 dengue attributes and weather data in 2020. K-means automatically searches for unknown clusters from dengue cases data quickly, which cluster results C1 (Sukajadi, Senapelan), C2 (Tenayan Raya, Tampan), and C3 (Rumbai Pesisir, Rumbai). Based on experimental testing, this study produced a silhouette score is 0.6. Meanwhile, the correlation matrix looks for relevant relationships hidden in the data. The correlation matrix obtained a strong linear relationship between the population (JP) and sufferers (P) of 0.73 for January and 0.93 for February 2020.Keywords— Dengue Fever, K-means, Correlation matrix, Machine learning.

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