Jurnal Pilar Nusa Mandiri
Jurnal Pilar merupakan jurnal ilmiah yang diterbitkan oleh program studi sistem informasi STMIK Nusa Mandiri. Jurnal ini berisi tentang karya ilmiah yang bertemakan: Rekayasa Perangkat Lunak, Sistem Pakar, Sistem Penunjang, Keputusan, Perancangan Sistem Informasi, Data Mining, Pengolahan Citra.
Articles
418 Documents
<![CDATA[DESIGN OF AN AUTOMATIC STERILIZATION GATE TOOL USING PIR MOTION SENSOR ]]>
<![CDATA[Amrullah, Faiq Fawwaz]]>;
<![CDATA[Khairani, Dewi]]>;
<![CDATA[Masruroh, Siti Ummi]]>
<![CDATA[ Jurnal Pilar Nusa Mandiri Vol 17 No 1 (2021): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri ]]>
Publisher : <![CDATA[LPPM Universitas Nusa Mandiri ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.33480/pilar.v17i1.1937
<![CDATA[This automatic sterilization gate tool is a simple tool that changes the manual method to a systemized and more efficient way. This tool uses an infrared motion sensor in which there is a main component, namely the PIR (Pyroelectric Infra-Red) sensor, which is a material that reacts to radiation and movement in front of it. This tool also uses a 12v dc pump which is used to draw out the disinfectant liquid stored in the container. This gate tool is designed using the main material and some additional materials, the automatic gate can discharge about 11 seconds of liquid from objects or people that pass through the sensor range, 11 seconds is the minimum time for this tool to work while a distance of 6 meters is the range from where the sensor is located. From that time it proved that this gate tool has 100% accuracy and runs well. This gate tool is used every day, turned on from the morning, and turned off at night. Every 17.00 hours this gate tool is always replenished with supplies of disinfectant liquid. The automatic gate has helped the community to reduce the risk of transmission of the coronavirus, it also raises the awareness of the covid-19 pandemic. The whole system is implemented and is tested for real-time operation. It is found working satisfactorily. The gate tool can be further improved by adding a scanning device to perform tracing for passersby.]]>
<![CDATA[DETECTION SYSTEM OF TEN FINGERPRINT PATTERN USING MATHEMATICAL MORPHOLOGY AND BACKPROPGATION ARTIFICIAL NEURAL NETWORK ]]>
<![CDATA[Lestari, Wiji]]>;
<![CDATA[Basiroh, Basiroh]]>;
<![CDATA[Widyaningsih, Pipin]]>
<![CDATA[ Jurnal Pilar Nusa Mandiri Vol 17 No 1 (2021): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri ]]>
Publisher : <![CDATA[LPPM Universitas Nusa Mandiri ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.33480/pilar.v17i1.1988
<![CDATA[This research has aim to produce for detection system of human ten fringerprints patterns that according to Dermatoglypic. The fringerprints patterns able to use for advanced analysis to biological and psychological characteristics. This research use back propagation algorithm of neural network to identify of fringerprint patterns. Initial processing is used mathematical morphology method before it is detected. The image is changed to digital image and then it is processed by dilation and erotion for enhancement image. The image that as neuron input of back propagation is changed to gray scale and 8 x 8 of size. Training process use 2000 epochs and patterns [200 2 1]. The output result are identification of human ten fringerprints patterns. This research produce identification are whorl, arch, right loop and left loop patterns of fringerprints. The result of research are whorl patterns 51.67%, right loop patterns 23.33% and left loop 18.33%. The accuration of detection system is 93.33%.]]>
<![CDATA[PIECES FRAMEWORK ON THE IMPLEMENTATION OF RAPID ENUMERATION AND EVALUATION INFORMATION SYSTEM OF 2020 POPULATION CENSUS ]]>
<![CDATA[Yuanita, Rizka Ita]]>
<![CDATA[ Jurnal Pilar Nusa Mandiri Vol 17 No 1 (2021): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri ]]>
Publisher : <![CDATA[LPPM Universitas Nusa Mandiri ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.33480/pilar.v17i1.2014
<![CDATA[In September 2020, Statistics Indonesia (BPS RI) carried out a major activity, namely the 2020 Population Census At the SP2020 data processing stage, Mobile Capture is one of the options for photographing data collection results. During the use of mobile capture, BPS West Java Province encountered several obstacles. West Java Province BPS created an application called the Rapid Enumeration dan Evaluation 2020 Population Census Information System (Sicepat32 SLS) to monitor the attainment of enumeration to the smallest level (local environmental unit). To find out whether Sicepat32 SLS is working and functioning properly, it is necessary to evaluate the performance of the information system. The purpose of this study is to measure the level of user satisfaction with the application of Sicepat32 SLS and to assess whether Sicepat32 SLS can meet the needs of users. In this study, the PIECES Framework analysis model will be used and has produced an assessment score in the Performance domain of 4.07; Information and Data of 4.17; Economy 4.1; Control and Security with a score of 4.03; Efficiency with a score of 4.18 and Service getting a score of 4.18]]>
<![CDATA[WEB-BASED RPS MANAGEMENT INFORMATION SYSTEM (SEMESTER LESSON PLAN) USING WATERFALL MODEL ]]>
<![CDATA[Nugraha, Muhammad]]>;
<![CDATA[Usman, Muhammad Lulu Latif]]>;
<![CDATA[Tiawan, Tiawan]]>
<![CDATA[ Jurnal Pilar Nusa Mandiri Vol 17 No 1 (2021): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri ]]>
Publisher : <![CDATA[LPPM Universitas Nusa Mandiri ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.33480/pilar.v17i1.2098
<![CDATA[Semester Learning Plan (RPS) is a learning planning document prepared as a guide for lecturers and students in carrying out teaching and learning activities on campus for one semester. RPS was made to guide lecturers and students in the learning process so that the material taught is following the learning outcomes set by the study program. To meet and adjust the needs of graduate users and the development of science and technology, the RPS must be regularly reviewed and updated if there are changes in teaching materials that are no longer following current conditions. However, what often becomes a problem is that there is no documented process for changing the RPS content regarding the reasons and which parts have changed so that the RPS change process cannot be carried out at will and when audited there is a history of changing the data. Because of these problems, this research will design and build a web-based RPS management information system that can assist study programs in documenting RPS changes and making it easier for users to access the changed content. The system development method used in this study uses the waterfall method, and for its design using UML (Unified Modeling Language). The final result of this research is a web-based RPS management information system using the Codeigniter 4 framework which can make it easier for managers and users to obtain RPS information.]]>
<![CDATA[TELEMARKETING BANK SUCCESS PREDICTION USING MULTILAYER PERCEPTRON (MLP) ALGORITHM WITH RESAMPLING ]]>
<![CDATA[Masturoh, Siti]]>;
<![CDATA[Nugraha, Fitra Septia]]>;
<![CDATA[Nurlela, Siti]]>;
<![CDATA[Saelan, M. Rangga Ramadhan]]>;
<![CDATA[Saputri, Daniati Uki Eka]]>;
<![CDATA[Nurfalah, Ridan]]>
<![CDATA[ Jurnal Pilar Nusa Mandiri Vol 17 No 1 (2021): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri ]]>
Publisher : <![CDATA[LPPM Universitas Nusa Mandiri ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.33480/pilar.v17i1.2168
<![CDATA[Telemarketing is a promotion that is considered effective for promoting a product to consumers by telephone, other than that telemarketing is easier to accept because of its direct nature of offering products to consumers. Telemarketing is also considered to help increase a company's revenue. The problem of predicting the success of a bank's telemarketing data must be done using machine learning techniques. Machine learning used in the available historical data is a bank dataset of 45211 instances at 17 features using the multilayer perceptron algorithm (MLP) with resampling. The use of resampling aims to balance the unbalanced data resulting in an accuracy value of 90.18% and a ROC of 0.89%. Meanwhile, if the data resampling is not used in the multilayer perceptron (MLP) algorithm, the accuracy value is 88.6 and ROC is 0.88%. The use of resampling data becomes more effective and results in higher accuracy values.]]>
<![CDATA[SENTIMENT ANALYSIS AGAINST THE DANA E-WALLET ON GOOGLE PLAY REVIEWS USING THE K-NEAREST NEIGHBOR ALGORITHM ]]>
<![CDATA[Masturoh, Siti]]>;
<![CDATA[Pohan, Achmad Baroqah]]>
<![CDATA[ Jurnal Pilar Nusa Mandiri Vol 17 No 1 (2021): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri ]]>
Publisher : <![CDATA[LPPM Universitas Nusa Mandiri ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.33480/pilar.v17i1.2182
<![CDATA[DANA e-Wallet or digital wallet application can be downloaded on the Android platform via Google Play, and google play itself provides a review column. The public will usually see reviews on Google Play before they download an application because the information obtained through these reviews is considered effective in providing information, problems regarding reviews or sentiment analysis of the application must be processed using text mining. Text mining in this study uses k-nearest neighbor by testing 3 classes based on star rating, the first class consists of 1-5 stars, the second class consists of (1 & 5 stars, 3rd class consists of labeling stars (1 & 2) negative label, 3 neutral labels, as well as 4 & 5 stars positive labels) and testing the value of k 1-10 so that the highest accuracy value is obtained with class 2 (1 star and 5 stars) and the best test at k 1 value is obtained the accuracy result of 86.64%]]>
<![CDATA[PREDICTION OF COOPERATIVE LOAN FEASIBILITY USING THE K-NEAREST NEIGHBOR ALGORITHM ]]>
<![CDATA[Roviani, Roviani]]>;
<![CDATA[Supriadi, Deddy]]>;
<![CDATA[Iskandar, Iqbal Dzulfiqar]]>
<![CDATA[ Jurnal Pilar Nusa Mandiri Vol 17 No 1 (2021): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri ]]>
Publisher : <![CDATA[LPPM Universitas Nusa Mandiri ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.33480/pilar.v17i1.2183
<![CDATA[Approval of credit lending to cooperative members without proper feasibility analysis can cause credit problems, cooperatives such as late payment of installments, and an increase in bad credit which can threaten the survival of the cooperative as a provider of lending services. As a solution to minimize the creditworthiness assessment errors for loan funds, research is carried out to analyze the feasibility of loan funds from the data of cooperative members using the data mining method approach and the algorithm used using the K-Nearest Neighbor. The purpose of this research is to predict the feasibility of granting credit with the right decision and to find out the level of evaluation, accuracy, and validation of the effectiveness of the k-NN algorithm on processing creditworthiness application data classifications. After the prediction research was carried out, the data on the eligibility of credit lending applications were conducted at the Bakti Berkah Sukaraja Savings and Loan Cooperative, The data obtained from the accuracy value of the k-nearest neighbor algorithm before being validated has an accuracy of 87.78% with AUC 0.95, after validation with split validation the accuracy decreased slightly by 2% to be 85.71%, while the AUC value in the ROC Curve was 0.836%. Even though there was a decline, it can still be categorized as a good classification. The impact of this research is that besides the accuracy of the k-NN algorithm being validated, the Bakti Berkah Sukaraja Savings and Loan cooperative can predict the feasibility of applying for credit funds, as an effort to reduce the threat of bad credit risk]]>
<![CDATA[GOVERNMENT POLICIES MODELING IN CONTROLLING INDONESIA'S COVID-19 CASES USING DATA MINING ]]>
<![CDATA[Enri, Ultach]]>;
<![CDATA[Sari, Eka Puspita]]>
<![CDATA[ Jurnal Pilar Nusa Mandiri Vol 17 No 1 (2021): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri ]]>
Publisher : <![CDATA[LPPM Universitas Nusa Mandiri ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.33480/pilar.v17i1.2206
<![CDATA[Since the positive case of covid-19 in Indonesia, the government has taken several policies with the purpose of controlling the spread of the covid-19 virus, which has been regulated in Government Regulation No. 21 of 2020. The purpose of research is to obtain a model of government policy in controlling cases of covid by using data mining classification techniques, and obtain attributes that have the greatest weight, as well as look at the impact of policies that have been carried out by the government on the cases of covid-19 in Indonesia. The methodology used in the research is Knowledge Discovery In Database (KDD). Based on the research that has been done, it can be concluded that the policies that have been done by the government in controlling cases of covid-19 can be said to be successful, the C4.5 algorithm is the algorithm that gives the best results compared to the Deep Learning algorithm, as well as the attribute that has the greatest weight is cancel public events. Secondary data will be used in this research.]]>
<![CDATA[DECISION SUPPORT OF CONTRACT EMPLOYEE PERFORMANCE ASSESSMENT USING SAW METHOD AT PT. AEROFOOD ACS ]]>
<![CDATA[Rahman, Fathur]]>;
<![CDATA[Syarifa, Naf'a]]>;
<![CDATA[Hendri, Hendri]]>;
<![CDATA[Novitasari, Hafifah Bella]]>;
<![CDATA[Gata, Windu]]>
<![CDATA[ Jurnal Pilar Nusa Mandiri Vol 17 No 1 (2021): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri ]]>
Publisher : <![CDATA[LPPM Universitas Nusa Mandiri ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.33480/pilar.v17i1.2034
<![CDATA[Pengelolaan SDM dari suatu perusahaan sangat mempengaruhi banyak aspek penentu keberhasilan kinerja perusahaan tersebut. Jika SDM dapat di organisir dengan baik, maka diharapkan perusahaan dapat menjalankan semua proses bisnisnya dengan baik. Oleh karena hal tersebut, PT. Aerofood ACS yang memiliki banyak karyawan kontrak, perlu adanya penilaian kinerja karyawan dalam menentukan perpanjangan kontrak. Peran sistem pendukung keputusan sangat dibutuhkan guna meningkatkan efisiensi pengambilan keputusan. Dalam hal ini membantu pihak manajemen dalam mencapai tujuan dari penilaian kinerja karyawan kontrak melalui parameter-parameter yang sudah ditentukan oleh pihak perusahaan tersebut, diantaranya Discipline, Integrity, Achievement Orientation, Continnous Learning, Continunous Improvement, Quality Orientation, Customer Service Orientation, dan Teamwork. Untuk mencari solusi dalam menyelesaikan masalah tersebut, metode dalam Sistem Pengambilan Keputusan yang digunakan yaitu dengan metode Simple Additive Weighting (SAW). Semua parameter yang dinyatakan mempunyai pengaruh penting dalam penetapan alternatif keputusan terbaik dalam menentukan perpanjangan kontrak karyawan.]]>
<![CDATA[COMPARISON OF DECISION TREE, NAÏVE BAYES, AND NEURAL NETWORK ALGORITHM FOR EARLY DETECTION OF DIABETES ]]>
<![CDATA[Septiani, Wisti Dwi]]>;
<![CDATA[Marlina, Marlina]]>
<![CDATA[ Jurnal Pilar Nusa Mandiri Vol 17 No 1 (2021): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri ]]>
Publisher : <![CDATA[LPPM Universitas Nusa Mandiri ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.33480/pilar.v17i1.2213
<![CDATA[Diabetes mellitus is included in the top 3 most deadly diseases in Indonesia. Based on WHO data in 2013, diabetes contributed 6.5% to the death of the Indonesian population. Diabetes is a chronic disease characterized by high blood sugar (glucose) levels that exceed normal limits. In the health sector, historical medical data can be processed to extract new information and can be used for decision-making processes such as disease prediction. This study aims to classify predictions for early detection of diabetes in order to obtain accurate results for decision making. The data used are historical data on hospital disease patients in Sylhet, Bangladesh in the form of a diabetes dataset from the UCI Repository. The algorithms used are Decision Tree, Naive Bayes, and Neural Network. Then the three methods are compared using the Rapidminer tools. The measurement results are 90% accuracy with Decision Tree, 80% with Naive Bayes, and 70% with Neural Network. So that the best algorithm is obtained, namely the Decision Tree for predicting early detection of diabetes. Rule in the form of a decision tree generated from the Decision Tree is used for input or ideas for decision making in the health sector for diabetes.]]>
