Jurnal Sistim Informasi dan Teknologi
The Jurnal Sistim Informasi dan Teknologi (JSISFOTEK) aims to publish manuscripts that explore information systems and technology research and thus develop computer information systems globally. We encourage manuscripts that cover the following topic areas: - Analytics, Business Intelligence, and Decision Support Systems in Computer Information Systems - Mobile Technology, Mobile Applications - Human-Computer Interaction - Information and/or Technology Management, Organizational Behavior & Culture - Data Management, Data Mining, Database Design and Development - E-Commerce Technology and Issues in computer information systems - Computer systems enterprise architecture, enterprise resource planning - Ethical and Legal Issues of IT - Health Informatics - Information Assurance and Security-Cyber Security, Cyber Forensics - IT Project Management - Knowledge Management in computer information systems - Networks and/or Telecommunications - Systems Analysis, Design, and/or Implementation - Web Programming and Development - Curriculum Issues, Instructional Issues, Capstone Courses, Specialized Curriculum Accreditation - E-Learning Technologies, Analytics, Future.
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<![CDATA[Simulasi dalam Menganalisis Tingkat Pendapatan Penjualan Produk Bengkel Las menggunakan Metode Monte Carlo ]]>
<![CDATA[Adya Prawira Asril]]>
<![CDATA[ Jurnal Sistim Informasi dan Teknologi 2023, Vol. 5, No. 1 ]]>
Publisher : <![CDATA[SEULANGA SYSTEM PUBLISHER ]]>
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DOI: 10.37034/jsisfotek.v5i1.155
<![CDATA[Bengkel Las Cahaya Teknik merupakan sebuah perusahaan yang bergerak dibidang produksi yang menjual berbagai produk besi diantaranya pagar besi, ayunan dan teralis jendela. Ketersediaan stok barang sangat berpengaruh pada jumlah pendapatan sebuah perusahaan. Permintaan produk yang sering berubah membuat pimpinan Bengkel Las Cahaya Teknik mengalami kesulitan dalam menentukan seberapa banyak stok yang harus disiapkan agar dapat mencapai penjualan yang maksimal. Tujuan dari penelitian ini adalah untuk memprediksi tingkat pendapatan perusahaan pada tahun berikutnya dengan menggunakan Metode Simulasi dengan Model Monte Carlo. Dengan Metode Monte Carlo, akan dilakukan pengujian data pendapatan tahun sebelumnya untuk memprediksi tingkat pendapatan tahun berikutnya. Data yang akan diuji merupakan data pendapatan perusahaan dalam tiga tahun terakhir yakni dari Januari 2019 sampai Desember 2021. Selain dapat memprediksi penjualan, hasil dari pengujian metode ini juga bisa digunakan sebagai acuan bagi pimpinan perusahaan untuk menyiapkan stok barang sehingga penjualan dapat dimaksimalkan. Berdasarkan pengujian yang telah dilakukan didapatkan hasil bahwa Metode Monte Carlo dapat memprediksi tingkat pendapatan penjualan produk Bengkel Las Cahaya Teknik dengan akurasi pada tahun 2019 akurasi mencapai 93,06%, pada tahun 2020 sebesar 98,18% dan tahun 2021 sebesar 92,33%. Dengan tingkat akurasi yang mencapai angka tersebut, penerapan metode ini dianggap mampu melakukan prediksi pendapatan terhadap bengkel las setiap tahunnya sehingga dapat membantu pemilik bisnis dalam memilih strategi bisnis yang lebih baik untuk meningkatkan pendapatannya.]]>
<![CDATA[Sistem Pakar Akurasi dalam Mengidentifikasi Penyakit Gingivitis pada Gigi Manusia dengan Metode Naive Bayes ]]>
<![CDATA[Rifa Yuliza]]>
<![CDATA[ Jurnal Sistim Informasi dan Teknologi 2023, Vol. 5, No. 1 ]]>
Publisher : <![CDATA[SEULANGA SYSTEM PUBLISHER ]]>
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DOI: 10.37034/jsisfotek.v5i1.157
<![CDATA[Gingivitis is a condition where the gums become inflamed due to a bacterial infection, causing the gums to swell. Gingivitis if treated too late will trigger more dangerous dental disease. Minimize knowledge about gingivitis and limited time to consult with experts so that people pay less attention to dental and oral health which can indicate gingivitis. The purpose of this study was to determine accuracy in identifying gingivitis disease using the Naive Bayes method, which can help the public to find out information about gingivitis, so a system with experts was built. Expert System is knowledge from experts that is entered into a computer or system that can be used for consultation. The data used in this study were 24 symptom data and 5 types of disease data sourced from interviews with an expert. The experts used in this study were dentists at the Rahmatan Lil Alamin Clinic. The data can be obtained from the results of the medical records of patients who perform examinations with dentists. The data processed in this study is knowledge about the symptoms and types of gingivitis in the teeth obtained from an expert. The result of the test is that the largest value of the total probability is found in the type of gum disease Puberty with a value of 0.000106795855. This research can help the community to conduct consultations easily, find out the symptoms early so that people do not have to go to the hospital with a long distance.]]>
<![CDATA[Prediksi Kuantitas Penggunaan Obat pada Layanan Kesehatan Menggunakan Algoritma Backpropagation Neural Network ]]>
<![CDATA[Fajrul Khairati]]>;
<![CDATA[Hasdi Putra]]>
<![CDATA[ Jurnal Sistim Informasi dan Teknologi 2022, Vol. 4, No. 3 ]]>
Publisher : <![CDATA[Rektorat Universitas Putra Indonesia YPTK Padang ]]>
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DOI: 10.37034/jsisfotek.v4i3.158
<![CDATA[Prediction of the amount of drug use at public health centers is needed to ensure the availability of drugs for patients in service quality management. A good prediction of the amount of medicine needed helps the quality of development planning in the health sector. Scientific developments in the field of Artificial Intelligence (AI) deliver a variety of the best techniques for making predictions. By adopting the workings of neural networks (neurons) in the human brain or Artificial Neural Network (ANN), the Backpropagation Neural Network (BPNN) algorithm is one of the best algorithms in making predictions, including predicting drug use in health services. The problem of this research is how to design the best architectural model such as the number of neurons in the input layer, hidden layer and other parameters so as to produce predictions with optimal accuracy. This study aims to develop an ANN architectural design with the Backpropagation algorithm to predict the need for drug use. The data used is data on drug use reports from 2015 to 2021 at the Andalas Community Health Center (Puskesmas) Padang City. The steps taken to predict are; collect data, pre-process data and perform analysis, design ANN architecture, make predictions. Learning using the backpropagation algorithm through the initial weight initialization process, activation stage, weight training (weight change) and iteration stage. The proportion of the amount of data used for training is 70% data and 30% for testing data. The results of this study indicate that the best ANN architecture is 12-12-1 with an accuracy of predicting the quantity of drug use reaching 97.87% for paracetamol with a Mean Absolute Percentage Error (MAPE) of 2.13%. The prediction results become a reference for the Puskesmas and the Health Office for service planning and development.]]>
<![CDATA[Sistem Deteksi Intrusi pada Server secara Realtime Menggunakan Seleksi Fitur dan Firebase Cloud Messaging ]]>
<![CDATA[Faizal Riza]]>
<![CDATA[ Jurnal Sistim Informasi dan Teknologi 2023, Vol. 5, No. 1 ]]>
Publisher : <![CDATA[SEULANGA SYSTEM PUBLISHER ]]>
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DOI: 10.37034/jsisfotek.v5i1.161
<![CDATA[Intrusion detection is one of the fundamental parts of a security tool, such as adaptive security tools, intrusion detection systems, intrusion prevention systems and firewalls. There are various kinds of intrusion detection techniques used, the main problem of this intrusion technique is the performance problem. The accuracy of the intrusion detection technique greatly affects its performance, which needs to be improved to reduce the false alarm rate and increase the detection rate. In solving performance problems, multilayer perceptron, support vector machine (SVM), and other techniques have been used recently. This technique shows limitations and is inefficient for use in large data sets, such as system and network data. Intrusion detection systems are used in analyzing a very large data traffic; thus, an efficient classification technique is needed to overcome these problems. This issue is considered in this paper. The well-known machine learning techniques, namely, SVM, random forest, and extreme machine learning will be applied. These techniques are well known for their ability to classify. NSL knowledge discovery and data mining datasets were used, which were considered as benchmarks in the evaluation of intrusion detection mechanisms. The results show that ELM outperforms other approaches. Utilization of Firebase Cloud Messaging because it can work with multiple platforms in addition to the availability of a file store that can store all logs created by the JALA application.]]>
<![CDATA[Klasterisasi Pasien Rawat Inap Peserta BPJS Berdasarkan Jenis Penyakit Menggunakan Algoritma K-Means ]]>
<![CDATA[Yandiko Saputra Sy]]>
<![CDATA[ Jurnal Sistim Informasi dan Teknologi 2023, Vol. 5, No. 2 ]]>
Publisher : <![CDATA[SEULANGA SYSTEM PUBLISHER ]]>
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DOI: 10.37034/jsisfotek.v5i2.162
<![CDATA[Medical records of patients from the Health Insurance Administering Body (BPJS) consist of complete patient data along with a complex history of patient services stored in every health facility. Inpatient medical record data contains important data as well as contains useful information as new knowledge using data mining techniques. This study aims to assist and provide new information related to the clustering of BPJS inpatients at the Arifin Achmad Hospital, Riau Province, so as to obtain information related to the spread of the patient's disease. The data used are medical records of inpatients in 2021. The data obtained are then processed using the K-Means clustering algorithm with a total of 3 clusters. The study resulted in cluster K1 dominated by Malignant neoplasm, breast, unspecified (C50.9) and Non-Hodgkin's lymphoma, unspecified type (C85.9) disease. Cluster K2 is dominated by fracture of neck of femur, closed (S71.00) and Dengue haemorrhagic fever (A91).]]>
<![CDATA[Analisis Predictive Maintenance Peralatan Lab Berbasis Machine Learning ]]>
<![CDATA[Wiki Lofandri]]>
<![CDATA[ Jurnal Sistim Informasi dan Teknologi 2023, Vol. 5, No. 1 ]]>
Publisher : <![CDATA[SEULANGA SYSTEM PUBLISHER ]]>
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DOI: 10.37034/jsisfotek.v5i1.164
<![CDATA[The rapid development of AI is also supported by our entry into the digital era and the Internet of Things (IoT). In using laboratory equipment, students are required to comply with the rules so that the equipment can be maintained properly. However, the tool used will cause thirst for the tool. This becomes a problem when the tool is needed for learning, while the tool does not function properly or is completely damaged. The research method used in Predicting Labor Equipment of the Department of Electronics FT-UNP is the Cross-Industry Standard Process for Data Mining (CRISP-DM) development method. CRISP-DM is a very reliable model. By utilizing Predictive Maintenance technology on each existing equipment, we can analyze data to identify damage with failure mode, so as to obtain data regarding the frequency of occurrence of damage and the severity of damage to labor equipment and setting a warning for labor technicians, at this time there will be an alarm for technicians to perform checking of equipment.]]>
<![CDATA[Metode Fuzzy untuk Mengidentifikasi Kepribadian Siswa ]]>
<![CDATA[Nanda Putra]]>;
<![CDATA[Ilham Danu Saputra]]>
<![CDATA[ Jurnal Sistim Informasi dan Teknologi 2022, Vol. 4, No. 3 ]]>
Publisher : <![CDATA[Rektorat Universitas Putra Indonesia YPTK Padang ]]>
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DOI: 10.37034/jsisfotek.v4i3.165
<![CDATA[Personality identification is one of the important things to know yourself and others. This identification is done based on the personality traits possessed by a person based on Kant's personality theory. Not a few teachers who do not understand the student's personality, in the teaching and learning process some teachers who do not understand the student's personality, a teacher will find it difficult to convey learning materials that will attract students' interest which has an impact on the knowledge transfer process being hampered. Then the Fuzzy Tsukamoto method is used to identify the student's personality. The purpose of this research is to help teachers in classifying and recognizing students' personalities so that it is easier to determine treatment in developing their talents and interests. The system input is obtained from personality traits that are suitable for students. The knowledge base was obtained from a Child Clinical Psychologist and was built with the qadidah (IF-THEN). The output of the Fuzzy calculation is that students have a Sanguine, Choleric, Melancholic or Phlegmatic personality. The results of testing this method by doing calculations and testing the system, it is obtained that personality outputs are in accordance with the personality characteristics of students and are running well. So it can be recommended to help teachers in determining the treatment of students.]]>
<![CDATA[Optimalisasi dalam Mengidentifikasi Seleksi Mahasiswa Jalur Cepat (Fast-track) Menggunakan Metode K-Nearest Neighbor ]]>
<![CDATA[Zumardi Rahman]]>
<![CDATA[ Jurnal Sistim Informasi dan Teknologi 2023, Vol. 5, No. 2 ]]>
Publisher : <![CDATA[SEULANGA SYSTEM PUBLISHER ]]>
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DOI: 10.37034/jsisfotek.v5i2.166
<![CDATA[Penerimaan fast-track dilakukan untuk membantu penyeleksian dalam memberikan rekomendasi mahasiswa yang berpotensi bergabung pada program fast-track maka dibutuhkan Sistem Pendukung Keputusan, dikarenakan sistem penyeleksian calon penerima mahasiswa fast-track masih manual, dan banyak sekali kelemahannya. Banyaknya peminat dalam mendaftar fast-track menyebabkan ketua jurusan mengalami kesusahan saat mengolah data yang manual sehingga dibutuhkan perangkat lunak untuk memudahkan pengolahan data tersebut. Tidak semua mahasiswa yang mengajukan permohonan untuk mendapatkan fast-track dapat disetujui, di karenakan mahasiswa yang mengajukan permohonan cukup banyak, maka begitu dibutuhkan sekali agar dibangun suatu SPK dengan metode K-Nearest Neighbor (K-NN) yang dapat membantu memberikan rekomendasi kepada peminat fast-track. Berdasarkan analisis terhadap SPK dengan metode K-NN ini dilakukan dengan cara observasi wawancara dan implementasi sistem. Dalam penilaian penerimaan fast-track dapat dijadikan dasar untuk memudahkan keputusan pada penyeleksian mahasiswa fast-track karena sistem dapat mengolah data dan menghasilkan informasi secara cepat, tepat dan konsisten kepada ketua jurusan terhadap mahasiswa untuk bergabung fast-track yang akan diberikan. Dapat membentuk suatu keputusan yang tepat, efektif dan efisien pada pengelolaan data seleksi penerimaan fast-track yang memang berpotensi diterima fast-track. Metode K-NN dapat digunakan untuk mengidentifikasi seleksi penerimaan mahasiswa fast-track, SPK dalam penilaian penyeleksian mahasiswa fast-track dapat memudahkan keputusan pada mahasiswa secara proporsional dengan berdasarkan hasil proses data mahasiswa meliputi indeks prestasi mahasiswa semester 1-6, jumlah sks sampai semester 6 dengan tepat dan akurat karena sistem dapat meminimalisir kesalahan dalam proses perhitungan normalisasi data.]]>
<![CDATA[Algoritma Backpropagation dalam Akurasi Memprediksi Kemunculan Titik Api (Hotspot) pada Wilayah Kerja Dinas Kehutanan ]]>
<![CDATA[Riska]]>
<![CDATA[ Jurnal Sistim Informasi dan Teknologi 2023, Vol. 5, No. 2 ]]>
Publisher : <![CDATA[SEULANGA SYSTEM PUBLISHER ]]>
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DOI: 10.37034/jsisfotek.v5i2.167
<![CDATA[Forest and land fires are an annual disaster issue in Indonesia. The forest area in West Sumatra is ± 2,286,883.10 Ha and 27% or an more than 630,695 Ha of forest area categorized as critical land that has the potential to burn and be damaged. Controlling for forest and land fires in West Sumatra Province was task for Forestry Departement, part of Sumatera Barat Government. One of is task was to reduce the rate of forest destruction. Forest and land fires are an annual disaster issue in Indonesia. The forest area in West Sumatra is ± 2,286,883.10 Ha and 27% or an more than 630,695 Ha of forest area categorized as critical land that has the potential to burn and be damaged. Controlling for forest and land fires in West Sumatra Province was task for Forestry Departement, part of Sumatera Barat Government. One of is task was to reduce the rate of forest destruction. Apart from to extinguishing forest fires directly at the hotspots, preventive action are needed to reduce the possibility of forest and land fires, and one of it is by predicting the possibility hotspots in the future. One of the methods used to predict the possibility hotspots is the use of artificial neural network Backpropagation, this is because Backpropagation has the ability to learn from existing data patterns to calculate the possibility of future events. Data of hotspots that have happened previously and several supporting variables such as air temperature, humidity, rainfall and wind speed, were analyzed and grouped as the basis for the formation of an artificial neural network and for further data training. This learning is done by testing several different network architectures. The results obtained from these tests are the Performance and MSE (Mean Squared Error) values for each network architecture. The test results for each architecture will be compared to determine the best architecture that produces the most accurate predictive value and the smallest MSE. The results of this prediction will later be used as one of the considerations for the Forestry Departement for planning forest and land fire control activities in their area.]]>
<![CDATA[Algoritma K-Means Clustering dalam Optimalisasi Komposisi Pakan Ternak Ayam Petelur ]]>
<![CDATA[Felka Andini]]>;
<![CDATA[Della Zilfitri]]>;
<![CDATA[Yosep Filki]]>;
<![CDATA[Muhammad Ridho]]>
<![CDATA[ Jurnal Sistim Informasi dan Teknologi 2023, Vol. 5, No. 2 ]]>
Publisher : <![CDATA[SEULANGA SYSTEM PUBLISHER ]]>
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DOI: 10.37034/jsisfotek.v5i2.168
<![CDATA[In Indonesia, the laying hens business sector experiences many obstacles, farmers often face instability between the price of chicken eggs and the price of feed which tends to always increase. The income received by farmers is not proportional to the cost of feed incurred. The production cost of laying hens can be reduced if there is an increase in feed efficiency. Maintenance of laying hens lies in the provision of feed, water, physical conditions and the state of the cage. Feed is the main source of energy for laying hens. The problem of feed in laying hens must meet the quality and quantity of the feed itself so that the effect is very real and clear on egg production. Feed nutrition must also meet the needs of laying hens. Feeding laying hens without paying attention to the quality of the feed can result in the growth and productivity of chickens being not optimal. Combining feed is an effort that can be made to produce a quality feed composition. This research was conducted to compile the composition of laying hens' feed using the K-Means Clustering algorithm. The K-Means Clustering method is an algorithm used by researchers to group or cluster data on laying hens feed into several clusters by using the nutritional content of each feed as an attribute. In this study, the data analyzed was data on the nutritional content of laying hens feed consisting of attributes such as protein, fat, crude fiber, calcium and phosphorus. This study will produce 3 clusters of feed types consisting of highly optimal clusters, optimal clusters and less than optimal clusters. This research is expected to be used as a recommendation by laying hens in compiling the composition of laying hens to maintain the quality of the eggs produced.]]>
