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Sistemasi: Jurnal Sistem Informasi

stmsi
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Published by Universitas Islam Indragiri

ISSN : 23028149 EISSN : 25409719 DOI : -

Core Subject : Science, Engineering,

Computer Science & IT Electrical & Electronics Engineering

Sistemasi adalah nama terbitan jurnal ilmiah dalam bidang ilmu sains komputer program studi Sistem Informasi Universitas Islam Indragiri, Tembilahan Riau. Jurnal Sistemasi Terbit 3x setahun yaitu bulan Januari, Mei dan September,Focus dan Scope Umum dari Sistemasi yaitu Bidang Sistem Informasi, Teknologi Informasi,Computer Science,Rekayasa Perangkat Lunak,Teknik Informatika

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Articles 1,011 Documents

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Pengaruh Perceived Enjoyment, Perceived Reciprocal Benefit, dan Learning Culture Terhadap Knowledge Sharing Debby Ummul Hidayah; Ika Romadoni Yunita; Masyruri Riska Maulana
Sistemasi: Jurnal Sistem Informasi Vol 10, No 3 (2021): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

AbstrakAdanya pandemik COVID-19 mengharuskan perkuliahan dilakukan secara online. Hal tersebut dialami juga oleh Universitas XYZ yang menerapkan sistem kuliah online menggunakan beberapa media seperti website kuliah online, google classroom, zoom, google meet, dan platform lainnya yang disesuaikan dengan kesepakatan antara dosen dengan mahasiswa. Namun berbagai permasalahan muncul terutama mekanisme knowledge sharing dalam perkuliahan online. Oleh sebab itu, peneliti melakukan uji dengan menggunakan variabel perceived enjoyment, perceived reciprocal benefit, dan learning culture guna mengetahui bagaimana pengaruh dari ketiga variabel independen tersebut terhadap variabel dependen. Metode penelitian yang digunakan yakni menggunakan pendekatan kuantitatif. Berdasarkan hasil sebaran kuesioner dengan jumlah sampel 160 dari mahasiswa program studi Sistem Informasi Universitas XYZ dan analisis data menggunakan SPSS 22, hasilnya menunjukkan bahwa variabel perceived enjoyment menjadi faktor utama yang berpengaruh terhadap knowledge sharing. Atau dapat dikatakan bahwa variabel perceived enjoyment berpengaruh positif terhadap variabel knowledge sharing. Kemudian variabel learning culture juga memiliki pengaruh posiftif terhadap knowledge sharing. Adapun variabel perceived reciprocal benefit tidak memiliki pengaruh yang kuat terhadap knowledge sharing.Kata kunci: Perceived enjoyment, perceived reciprocal benefit, learning culture, knowledge sharing, kuliah online AbstractThe existence of the COVID-19 pandemic requires learning to be conducted online. This is also experienced by XYZ University which implements an online learning system using several media such as online learning websites, google classroom, zoom, google meet, and other platforms that are adjusted to the agreement between lecturers and students. However, various problems arise, especially the knowledge sharing mechanism in online learning. Therefore, the researcher conducted a test using the variables perceived enjoyment, perceived reciprocal benefit, and learning culture to find out how the influence of the three independent variables on the dependent variable. The research method used is a quantitative approach. Based on the results of the distribution of the questionnaire with a sample of 160 from students of the Information Systems study program at XYZ University and data analysis using SPSS 22, the results show that the perceived enjoyment variable is the main factor that influences knowledge sharing. Or it can be said that the perceived enjoyment variable has a positive effect on the knowledge sharing variable. Then the learning culture variable also has a positive influence on knowledge sharing. The perceived reciprocal benefit variable does not have a strong influence on knowledge sharing.Keywords: Perceived enjoyment, perceived reciprocal benefit, learning culture, knowledge sharing, online learning

Penerapan Algoritma K-Medoids Untuk Menentukan Segmentasi Pelanggan Anggi Ayu Dwi Sulistyawati; Mujiono Sadikin
Sistemasi: Jurnal Sistem Informasi Vol 10, No 3 (2021): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

AbstrakStrategi pemasaran berorientasi pelanggan memiliki peranan penting dalam mengelola hubungan baik dengan pelanggan. Agar strategi pemasaran tepat sasaran, segmentasi pelanggan dapat digunakan untuk mengelompokkan pelanggan berdasarkan karakteristik yang sama. Dalam penyusunan strategi pemasaran dapat memanfaatkan TI di bidang komputasi, salah satunya adalah data mining. Pemanfaatan teknologi komputasi untuk pengolahan data yang belum maksimal mengakibatkan penumpukan data yang miskin informasi. Pada penelitian ini dilakukan penerapan teknik clustering dengan menggunakan algoritma K-Medoids pada dataset transaksi penjualan untuk menentukan segmentasi pelanggan. Penyusunan strategi pemasaran ditentukan berdasarkan tipe dan karakteristik pelanggan pada setiap cluster atau segmen pelanggan yang terbentuk. Uji validitas cluster menggunakan Silhouette Index dan Davies Boulbin Index dilakukan untuk menentukan jumlah cluster yang paling optimal. Hasil penelitian ini menunjukan bahwa jumlah cluster optimal adalah 3 (tiga) cluster dengan nilai maksimum Silhouette Index adalah 0,375 dan nilai minimum Davies Doulbin Index adalah 1,030. Segmen pelanggan hasil penelitian adalah lost customer, core customer, dan new customer. Kata kunci: algoritma k-medoids, clustering, data mining, segmentasi pelanggan, strategi pemasaran AbstractCustomer-oriented marketing strategies play an important role in managing good relationships with customers. To keep marketing strategies on target, customer segmentation can be used to group customers based on the same characteristics. In the preparation of marketing strategies can utilize IT in the field of computing, one of which is data mining. The utilization of computing technology for data processing that has not been maximized resulted in a poor accumulation of information data. In this study, the application of clustering techniques using the K-Medoids algorithm on sales transaction dataset to determine customer segmentation. The preparation of a marketing strategy is determined based on the characteristics and types of customers in each cluster or segment of customers formed. cluster validity tests using the Silhouette Index and Davies-Boulbin Index are performed to determine the most optimal number of clusters. The results of this study showed that the optimal number of clusters is 3 (three) clusters with a maximum silhouette index value of 0.375 and the minimum value of the davies-bouldin index is 1.030. The customer segments of the research results are lost customers, core customers, and new customers. Keywords: k-medoids algorithm, clustering, data mining, customer segmentation, marketing strategy

Komparasi Algoritma C4.5 Dan Naïve Bayes Dalam Penentuan Status Kelayakan Donor Darah Kartika Handayani; Lisnawanty Lisnawanty; Abdul Latif; Muhammad Rifqi Firdaus; Fuad Nur Hasan
Sistemasi: Jurnal Sistem Informasi Vol 10, No 3 (2021): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Donor darah merupakan kegiatan kemanusiaan dimana seseorang dengan sukarela AbstrakDonor darah merupakan kegiatan kemanusiaan dimana seseorang dengan sukarela menyumbangkan darahnya untuk disimpan di bank darah yang kemudian digunakan untuk transfusi darah. UDD (Unit Donor Darah) PMI Kota Pontianak merupakan tempat pelayanan donor darah dari masyarakat Kota Pontianak. Dalam prakteknya, tidak semua masyarakat yang ingin mendonorkan darah dapat berhasil mendonorkan darahnya. Dalam memprediksi layak atau tidaknya masyarakat untuk mendonorkan darahnya dapat dilakukkan dengan klasifikasi data mining untuk mengetahui faktor yang paling mempengaruhi prediksi donor darah. Penelitian ini menggunakan metode klasifikasi algoritma C4.5 dan Naïve Bayes kemudian dilakukan perbandingan dua metode tersebut menggunakan confusion matrix, AUC dan uji beda t-test dengan analisa software rapidminer berdasarkan umur, jenis kelamin, berat badan, tekanan darah, dan hemoglobin. Dari hasil penelitian ini, hemoglobin adalah variabel paling menentukan kelayakan donor darah kemudian tekanan darah. Algoritma terbaik dalam kasus ini adalah Naïve Bayes dengan akurasi 93,26%, sedangkan tingkat akurasi C4.5 93,22%. Naïve Bayes termasuk dalam predikat good classsification dengan AUC sebesar 0.833, sedangkan C4.5 termasuk dalam predikat fair classsification dengan AUC sebesar 0.758. Dari hasil uji beda t-test diperoleh hasil 0.841 yang menyatakan bahwa tidak ada perbedaan signifikan dalam penentuan klasifikasi status kelayakan donor darah untuk kedua algoritma.Kata kunci: prediksi, donor darah, c4.5, naïve bayes AbstractBlood donation is a humanitarian activity in which someone voluntarily donates blood to be stored in a blood bank which is then used for blood transfusions. UDD (Blood Donation Unit) PMI Pontianak City is a blood donor service area of the Pontianak City community. In practice, not all people who want to donate blood can successfully donate blood. In predicting the feasibility of whether or not the community to donate blood can be done with the classification of data mining to determine the factors that most influence the prediction of blood donors. This study uses the C4.5 algorithm and Naïve Bayes classification method, then compares the two methods using a confusion matrix, AUC and t-test different test with rapidminer software analysis based on age, sex, weight, blood pressure, and hemoglobin. From the results of this study, hemoglobin is the most determining variable of eligibility for blood donation then blood pressure. The best algorithm in this case is Naïve Bayes with an accuracy of 93.26%, while the accuracy rate of C4.5 is 93.22%. Naïve Bayes is included in the category of good class certification with AUC of 0.833, while C4.5 is included in the category of fair class certification with AUC of 0.758. From the results of the t-test different test results obtained 0.841 which states that there is no significant difference in determining the classification of blood donor eligibility status for the two algorithms.Keywords: prediction, blood donor, c4.5, naïve bayes

Rancang Bangun Aplikasi Pelayanan Servis Kendaraan Galih Priyo Waseso; Gugun Gunawan; Muhammad Reza Ramdani; Yudo Devianto
Sistemasi: Jurnal Sistem Informasi Vol 10, No 3 (2021): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

AbstrakPerkembangan teknologi saat ini untuk menggunakan bantuan komputer sebagai salah satu pendukung sistem informasi dan entri data sangat penting dalam dunia bisnis khussnya di bengkel mobil. Berdasarkan pertiimbangan tersebut, perlu diperlukan aplikasi service kendaraan kerja untuk mencatat dan mengelola data service kendaraan kerja untuk mencatat dan mengelola data service perbaikan dan perawatan kendaraan. Kondisi tersebut mendorong kami untuk mengajukan pembuatan suatu aplikasi yang memanfaatkan teknologi informasi untuk membantu PT. KAR Bodyworks dalam melayani pelanggan. Aplikasi yang kami usulkan berupa NGEBENGKEL yaitu suatu aplikasi pelanggan yang berbasis Android. Aplikasi bengkel mobil berbasis android yang perangkat lunak ini dirancang menggunakan perangkat lunak web apche, database MYSQL dan Visual Studio Code Analisis kebutuhan diperlukan untuk membuat sistem dengan menggunakan pendekatan pengembangan waterfal. Tahap pembuatan aplikasi adalah analisis sistem saat ini, analisis proses bisnis yang di usulkan. Pada analisa proses bisnis telah dilakukan beberapa kegiatan, yaitu identifikasi masalah, analisa kebutuhan, perancangan UML, perancangan basis data, dan perancangan antarmuka.Kata kunci: servis online, kar bodyworks, aplikasi bengkel AbstractThe use of IT-enable devices in all current technological developments, information systems and data entry support is essential in the bussiness world, especially in auto repair shops. Based on these considerations, it is necessary to have a vehicle service application that functions to record and manage data on repair services and vehicle services. These conditions prompted us to submit an application that utilizes information technology to help PT. KAR Bodyworks in serving customers. The application we propose is NGEBENGKEL, which is an Android-based customer aplication. This Android-based car repair information system is designed using Apache web software, MYSQL database, and Visual Studio Code. Analysis of the requirements needed in making the system, using the waterfall development method. The steps taken to create the application are analyzing the running system, the proposed business process analysis. In business process analysis, several activities have been carried out, namely problem identification, needs analysis, UML design, database design, and interface design.Keywords: online service, kar bodyworks, workshop aplication

Factor Analysis of Kemendikbud's Free Internet Quota on the Online Learning Process Marshel Aditya Prayoga; Rinabi Tanamal
Sistemasi: Jurnal Sistem Informasi Vol 11, No 1 (2022): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Covid-19 emerged from a widespread virus originated from the Wuhan, Chinese, which caused a pandemic on a large scale, and hit various countries, especially in Indonesia. The Covid-19 pandemic that hit Indonesia made several aspects of life change significantly, especially in the field of education. Changes in the educational process that were previously face-to-face, are transformed into distance learning or online. Various needs emerged in this online learning process, such as the availability of internet network access was not smooth, the problem of the cost of buying internet quota, and limits on access to internet quotas obtained. Kementerian Pendidikan dan Kebudayaan (Kemendikbud) as the regulator of learning activities in Indonesia, strives for the smooth running of online learning activities at all levels, by distributing internet data packages to educators and students. Based on program from Kemendikbud, this research is expected to see the various factors that influence in the free internet quota program affect the online learning process for students at Universitas Ciputra Surabaya. The method used to collect survey data in this study is a questionnaire, and determining respondents using simple random sampling method, instrument distributed online to 90 users of free internet quota from Kemendikbud, because the Covid-19 pandemic is still not over. After the required data has been collected, validity and reliability tests are carried out and hypothesis testing is carried out using the SEM-PLS method using SmartPLS 3.3.3 software. The results of the tests carried out stated that there was a positive and significant influence given by the Benefit (KB) variable on Online Learning (PD) with a relationship value of 0.476, then there was a positive but insignificant effect given by the Network Quality (KJ) variable on the Learning variable. Online (PD) with a relationship value of 0.113, then there is a positive and significant influence provided by the Ease (KM) variable on the Online Learning (PD) variable with a relationship value of 0.355.

Big Data Infrastructure Design Optimizes Using Hadoop Technologies Based on Application Performance Analysis Shafiyah Shafiyah; Ahmad Syauqi Ahsan; Rengga Asmara
Sistemasi: Jurnal Sistem Informasi Vol 11, No 1 (2022): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Big data's infrastructure is a technology that provides the ability to store, process, analyze, and visualize large data. The tools and applications used are one of the challenges when building big data's infrastructure. In the study, we offered a new strategy to optimize big data infrastructure design that was an essential part of big data processing by performing performance analysis applications used at each stage of big data processing. The process started from collecting data sourcing online news using web crawler methods using Scrapyand Apache Nutch. Next, implement Hadoop technologies to facilitate the distribution of big data storage and computing. No-sql databases Mongo DB and HBase made it easier to query data, after which they built search engines using Elasticsearch and Apache Solr. Data saved later in analysis using hive apache, pig, and spark. The data has been analyzed was shown on the website using Zeppelins, Metabolase, Kibana, and Tableau. The test scenario consisted of the number of servers and files used. Testing parameters started from process speed, memory usage, CPU usage, throughput, etc. The performance testing results of each application were compared to and analyzed to see the merits and defaults of the application as a reference to building optimal infrastructure design to meet the needs of the user. This research has produced two big data infrastructure design alternatives. The suggested infrastructure has been implemented on computer nodes in the big data pens for processing big data from online media and proving to be running well.

Implementation of Dijkstra Algorithm with React Native to Determine Covid-19 Distribution Rosyid Ridlo Al Hakim; Purwono Purwono; Yanuar Zulardiansyah Arief; Agung Pangestu; Muhammad Haikal Satria; Eko Ariyanto
Sistemasi: Jurnal Sistem Informasi Vol 11, No 1 (2022): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Since Covid-19 was declared a global pandemic because it has spread throughout the world, every effort has been made to help prevent and tackle the transmission of Covid-19, including information technology. Information technology developed to determine the shortest distance for Covid-19 cases around us needs to be developed. This research implements Dijkstra's Algorithm written in the React Native programming language to build a Covid-19 tracking application. The system can display the closest distance with a radius of at least one meter, and the test results can map the nearest radius of 41 meters and the most immediate radius of 147 meters. This system is built for the compatibility of Android OS and iOS applications with React Native programming.

Quality Classification of Palm Oil Products Using Naïve Bayes Method Des Suryani; Ana Yulianti; Elsa Lutfi Maghfiroh; Jepri Alber
Sistemasi: Jurnal Sistem Informasi Vol 11, No 1 (2022): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Indonesia is a country that can produce the highest palm oil in the world. The islands of Sumatra and Kalimantan are the islands that have the largest plantations, especially oil palm in Indonesia. Riau Province which is located on the island of Sumatra can produce the highest oil palm on the island of Sumatra. Quality is an important component in business continuity in the palm oil industry. The quality of the crude palm oil products of a company in the Kerumutan sub-district, Pelalawan district, Riau, depends on the content of the final processing product. The content consists of impurities in crude palm oil (CPO), CPO moisture content, free fatty acid CPO levels, determination of bleachability index CPO, CPO carotene, dirt kernel, moisture kernel, and broken kernel. The final quality of palm oil products is determined from the combined results of CPO quality and kernel quality. Good quality raw materials will affect the selling price of these raw materials to produce a good quality final product. Determine the quality of palm oil products poses a problem in terms of time because they must be checked one by one through processing in the laboratory. Application builder who can determine the quality classification of palm oil products is the purpose of this research. This application is expected to help labor officers in the process of classifying the quality of crude palm oil products more quickly, precisely, and accurately. This study uses the Naïve Bayes algorithm because it requires smaller amounts of training data in the data classification process. The accuracy level of the Naïve Bayes method in determining the quality of palm oil products is 82.05%.

Analysis of Chatbot Response Constancy Using Boyer Moore Algorithm Aldis Gandi Mitra Sanjung; Norhikmah M.Kom (SCOPUS ID: 57216417658)
Sistemasi: Jurnal Sistem Informasi Vol 11, No 1 (2022): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Amikom Computer Club or commonly referred to by the name AMCC is one of the scientific student activity units at Amikom University Yogyakarta. AMCC consists of administrators and members. As the number of AMCC members increases every year, the administrators have difficulty in providing services in answering member questions quickly according to members' needs through the telegram sample chat application. Therefore, a Chatbot system is needed that is built to assist administrators in answering various questions from members using the Boyer Moore Algorithm, by the way the algorithm works that moves to compare characters from right to left or called string matching, thus shortening the information search time. The results of this study are that the chatbot system can respond well to member questions, and the results are tested using the User Acceptence Test, the chatbot only fails to answer 4 questions out of a total of 50 questions, and gets 70% accuracy by testing using the confusion matrix method. AMCC

Prediction of Mortlity Rate in Indonesia due to Covid-19 Using the Naïve Bayes Algorithm Abdi Rahim Damanik; Dedy Hartama; Irfan Sudahri Damanik
Sistemasi: Jurnal Sistem Informasi Vol 11, No 1 (2022): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

One of the functions of this research is to obtain the latest information regarding the level of accuracy and death rates due to the Covid-19 pandemic. One of the tasks of planning a response to a pandemic is to access data related to the number of deaths due to Covid-19. The research that the author is carrying out will predict the death rate due to the COVID-19 pandemic in Indonesia. This study collects all data sourced from the website address https://sinta.ristekbrin.go.id/covid/datasets. By using Indonesia's death rate data due to covid-19 from March 2020 to July 2021. The calculation process and prediction workflow will use the Naïve Bayes Algorithm to be able to measure accuracy and predict the death rate due to the coronavirus in 2022. Prediction testing data figures with a total of 20 the area is in the highest class with a death rate of 120,568 cases obtained based on the calculation of the Naive Bayes algorithm, for an accuracy performance of 100% by testing using Rapidminer tools. It is hoped that the results of this prediction can be used by the government to overcome and set plans for good improvements to the community during the coronavirus pandemic.

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