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[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[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.]]>
<![CDATA[Sistem Pendukung Keputusan dengan Metode Profile Matching dalam Mengidentifikasi Gejala Awal Penderita COVID-19 ]]>
<![CDATA[Jelviana Risa]]>
<![CDATA[ Jurnal Sistim Informasi dan Teknologi 2023, Vol. 5, No. 2 ]]>
Publisher : <![CDATA[SEULANGA SYSTEM PUBLISHER ]]>
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DOI: 10.37034/jsisfotek.v5i2.169
<![CDATA[The COVID-19 pandemic has not yet subsided, this outbreak has spread to almost all countries in the world, the initial symptoms of sufferers caused by acute respiratory distress coronavirus 2 (SARS-CoV-2). Symptoms of COVID-19 that can be transmitted from human to human, when one person is exposed to signs of a contagious COVID-19 occurring in the community is not the right attitude and action but panic and worry. The initial symptoms of COVID-19 have criteria that identify the initial symptoms of COVID-19, which are 10 (ten) criteria consisting of fever, cough and sore throat, fatigue, loss of smell and taste, joint and muscle pain, headache, diarrhea, shortness of breath. breath, nausea, vomiting, and nasal congestion. The purpose of this study was to identify the early symptoms of COVID-19 sufferers. This research was conducted through processing data on COVID-19 patients sourced from UPT Puskemas VI Koto Selatan, based on the results of the identification of symptoms of COVID-19 in patients carried out by health workers on duty at UPT Puskesmas VI Koto Selatan, then the data was processed using Support System Software. The decision to know the early symptoms of COVID-19. Furthermore, mathematical calculation techniques are also used to see the accuracy results. The method used to determine the initial symptoms in patients with COVID-19 is the Profile Matching method. The results of this study there were 6 patients from 8 test data that had the same decisions generated by the system, therefore the conclusion from this study was the results of the Decision Support System testing that had been carried out in identifying the initial symptoms of COVID-19 sufferers at UPT Puskemas VI South Koto overall there are 75% of patient data indicated by COVID-19 and 25% of patient data not indicated by COVID-19]]>
<![CDATA[Data Mining Tingkat Kepatuhan Pasien Tuberkulosis dalam Menjalani Pengobatan Mengunakan Agloritma C4.5 ]]>
<![CDATA[Muhammad Ridho]]>;
<![CDATA[Della Zilfitri]]>;
<![CDATA[Felka Andini]]>;
<![CDATA[Yosep Filki]]>
<![CDATA[ Jurnal Sistim Informasi dan Teknologi 2023, Vol. 5, No. 2 ]]>
Publisher : <![CDATA[SEULANGA SYSTEM PUBLISHER ]]>
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DOI: 10.37034/jsisfotek.v5i2.170
<![CDATA[The high number of TB cases in the work area of the Bukittinggi City Health Service Puskesmas Nilam Sari. The number of patients who do not comply with TB treatment. This study was conducted to determine the level of patient compliance in undergoing TB treatment so that the results of the study become input for medical personnel in charge of TB at Nilam Sari Health Center in policy making. The C4.5 method was used in this study to classify the data of compliant and non-adherent TB patients in undergoing treatment at the Nilam Sari Health Center. The data from TB patient visits to the Puskesmas were analyzed using the C4.5 method to obtain new knowledge from the TB patient visit data to the Puskesmas. The data analyzed consisted of attributes of the visit schedule, environmental distance, age which influenced the decision criteria for the level of adherence of TB patients in undergoing treatment at the Nilam Sari Health Center. The decision criteria for the results of TB patient visits consist of "Complied" and Non-Complied" which refers to the decision criteria for the TB patient's visit schedule. Tests conducted on the training data of the visit schedule of the attribute that most influence the decision on the level of adherence of TB patients in undergoing treatment. The implementation of the results using Weka 3.6.9 software and produces an accuracy of compliant patients of 13.4615% and accuracy of non-adherent patients of 86.5385%. The results of the classification method C.4.5 were greater in patients who were not compliant than patients who were obedient in undergoing TB treatment at the Nilan Sari Health Center. The test results have been able to help medical personnel in the Bukittinggi City Health Office work area in undergoing treatment to be able to make a policy for handling TB cases in the future.]]>
<![CDATA[Metode K-Means Clustering dalam Optimalisasi Kinerja Dosen Pendamping Akademik pada Program Kampus Merdeka ]]>
<![CDATA[Siti Fathuroh]]>
<![CDATA[ Jurnal Sistim Informasi dan Teknologi 2023, Vol. 5, No. 2 ]]>
Publisher : <![CDATA[SEULANGA SYSTEM PUBLISHER ]]>
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DOI: 10.37034/jsisfotek.v5i2.172
<![CDATA[Penelitian kinerja terhadap Dosen Pendamping Akademik (DPA) pada pelaksanaan Program Kampus Merdeka dilakukan setiap semesternya, dan masih mengolah data penilaiannya pendamping akademik secara manual yang mana dalam mengevaluasi tingkat kinerja pendamping akademik tersebut membutuhkan waktu yang cukup lama untuk mendapatkan hasil yang baik. Data penilaian kinerja pendamping akademik di peroleh dari Sekolah Tinggi Keguruan Ilmu Pendidikan Muhammadiyah Muara Bungo Kabupaten Bungo Provinsi Jambi. Data Dosen di ambil sebanyak 28 data, selanjutnya data penilaian tersebut diolah dengan metode K-Means dan diuji dengan Software RapidMiner. Proses yang digunakan untuk mengolah data Input dan teknik Clustering dengan algoritma K-Means. Kebutuhan input sistem yang dibangun membutuhkan input, yaitu : data penilaia kinerja Dosen Pendamping Akademik (DPA). Hasil penilian Kinerja Dosen Akademik (DPA) dengan metode ini membagi tiga kelas hasil penilaian, dan dengan perkiraan hasil yaitu kinerja sangat baik 43%, Kinerja baik 50% dan kinerja buruk 7% dan tingkat keberhasilan sampai 92% dengan mengguakan metode ClusteringI dan Algoritma K-Means dapat membatu pengambilan keputusan Kepala Biro Pendidikan Sekolah Tinggi Keguruan Ilmu Pendidikan Muhammadiyah Muara Bungo untuk membuat rekomendasi penilaian inerja Dosen terhadap mahasiswa di semester selanjutnya.]]>
<![CDATA[Optimalisasi dalam Seleksi Bibit Kelapa Sawit Unggul Menggunakan Metode TOPSIS ]]>
<![CDATA[Ahmad Rafi Rusydi]]>
<![CDATA[ Jurnal Sistim Informasi dan Teknologi 2023, Vol. 5, No. 2 ]]>
Publisher : <![CDATA[SEULANGA SYSTEM PUBLISHER ]]>
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DOI: 10.37034/jsisfotek.v5i2.173
<![CDATA[The quality of palm oil that will be produced from oil palm has a great influence on the type of oil palm seeds planted. The process from the maintenance of oil palm, to the process of processing palm oil into palm oil. The purpose of this research is to build a decision support system that can assist in the selection of superior oil palm seeds based on the website. The method used is Technique for Others Preference by Similarity to Ideal Solution (TOPSIS) which is a multi-criteria decision-making method. The principle of this method is that the chosen alternative must have the closest distance to the positive ideal solution and the farthest distance to the negative ideal solution. In this method, you can choose or make decisions by determining the weighting value for each attribute and then proceed with a ranking process that will provide the best alternative from one with the alternative with the highest priority value. This method provides an ideal solution for farmers in making decisions about which superior oil palm seeds to plant. The processed data is divided into 3 varieties, namely Dura, Pisifera, and Tenera. Each variety has different characteristics. The results of the research can be ranked correctly as a reference in determining decision making in the selection of the best seeds. This research can be used as a recommendation in the calculation for the selection of superior oil palm seeds.]]>
<![CDATA[Data Mining Performance Assessment of Regional Development Planning Agency Employees Using Bayesian Classifier Algorithm ]]>
<![CDATA[Nurfiah]]>;
<![CDATA[Khairul Zaman]]>
<![CDATA[ Jurnal Sistim Informasi dan Teknologi 2023, Vol. 5, No. 2 ]]>
Publisher : <![CDATA[SEULANGA SYSTEM PUBLISHER ]]>
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DOI: 10.37034/jsisfotek.v5i2.175
<![CDATA[Performance is the success or work achievement of a person or group in an organization in completing work. Performance is the goals and targets given, performance appraisal is carried out. The results of employee performance consist of two, namely good or bad. These results are used as indicators in making decisions for giving rewards or punishments. This study aims to measure the level of employee performance with targets very good, good, quite good, less good and bad. The data used in this study is employee data. The assessment criteria are the results of the best employee assessment at the Regional Development Planning Agency in June 2022. The method used in determining employee performance is the Bayesian Classifier Algorithm. This algorithm uses the concept of classification. The data that is processed is first classified and followed by the analysis process in producing employee performance. This study uses training data as many as 43 records then the assessment is used as testing data. The results of the analysis of employee performance appraisal using the Bayesian Classifier Algorithm that the algorithm succeeded in classifying employee performance in accordance with the objectives of this study very well.]]>
<![CDATA[Perancangan Sistem Informasi Praktek Kerja Industri pada SMKTI Bali Global Denpasar ]]>
<![CDATA[Erik Cahya Pradana]]>;
<![CDATA[I Wayan Gede Narayana]]>;
<![CDATA[I Made Arya Budhi Saputra]]>
<![CDATA[ Jurnal Sistim Informasi dan Teknologi 2023, Vol. 5, No. 2 ]]>
Publisher : <![CDATA[SEULANGA SYSTEM PUBLISHER ]]>
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DOI: 10.37034/jsisfotek.v5i2.223
<![CDATA[Praktek Kerja Industri (Prakerin) merupakan suatu kegiatan pendidikan dan pembelajaran yang dilakukan pada dunia Industri dalam upaya pendekatan atau untuk meningkatkan mutu para siswa SMK. Untuk menunjang hal tersebut, siswa kelas XI pada SMKTI Bali Global Denpasar diwajibkan untuk melaksanakan prakerin di salah satu perusahaan selama kurang lebih tiga bulan. Salah satu permasalahan yang terjadi pada SMKTI Bali Global Denpasar adalah dalam mengelola data prakerin masih dilakukan secara manual. Pengelolaan data manual tersebut dan banyaknya siswa yang terdiri dari 5 jurusan dan 14 kelas yang berbeda mengakibatkan bagian humas kesulitan dalam proses rekap data prakerin dari setiap kelas, bahkan seringkali mengalami data tertukar sehingga hasil rekaptulasi menjadi tidak maksimal dan terlambat. Pengelolaan dokumen yang masih dilakukan secara manual, juga mengakibatkan kesulitan saat mencari data tersebut ketika sewaktu-waktu diperlukan. Penelitian ini dilakukan dengan tujuan untuk membantu SMKTI Bali Global Denpasar untuk mencari solusi dan membantu atas permasalahan yang dihadapi. Pada penelitian ini dilakukan dengan menggunakan metode waterfall. Sistem ini dibangun dengan berbasis website dan menggunakan framework Laravel dan LeafletJS. Sistem ini dirancang menggunakan Diagram Use Case, Activity Diagram dan Entity Relationship Diagram (DFD). Lalu sistem diuji menggunakan metode pengujian blackbox testing. Penelitian ini menghasilkan suatu sistem informasi prakerin yang dapat digunakan untuk membantu bagian humas dan guru pembina dalam mengelola dan menyimpan data prakerin serta membantu siswa dalam mendapatkan informasi yang berkaitan dengan prakerin dengan lebih mudah dan jelas.]]>
