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"Vol. 6 No. 2 (2022): METHOMIKA: Jurnal Manajemen Informatika "
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<![CDATA[SISTEM PERINGATAN DINI BANJIR BERBASIS MACHINE LEARNING: STUDI LITERATUR ]]>
<![CDATA[Agustina Rachmawardani]]>;
<![CDATA[Sastra K. Wijaya]]>;
<![CDATA[Ardhasena Shopaheluwakan]]>
<![CDATA[ METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 6 No. 2 (2022): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi ]]>
Publisher : <![CDATA[Universitas Methodist Indonesia ]]>
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DOI: 10.46880/jmika.Vol6No2.pp188-198
<![CDATA[Indonesia is a disaster-prone country and 76% is a hydrometeorological disaster (floods, landslides, tropical cyclones and droughts). Floods that occurred in Jakarta had a negative impact on the community so that it would have an impact on economic losses that made it difficult for communities around areas that were frequently affected by floods to develop more advanced and productively. Therefore, the inhibition of increasing welfare caused by floods that are not immediately handled can increase the number of people's poverty because they always have to spend both on house repairs, health and other things caused by floods. In addition, public facilities and various kinds of infrastructure were damaged. The environment is also negatively affected when floods occur. Clean water is difficult to obtain so it causes many diseases. Floods also cause animals to be killed, thereby disrupting the natural balance of the ecosystem. The existence of flood prediction research will reduce the risk and damage caused by flood disasters and can provide advice and considerations in policy making. Flood early warning system is one of the solutions offered in dealing with flood disasters. Providing actual and real time information, this early warning system is expected to reduce economic losses to fatalities. In an effort to create a resilient city, ESCAP (2008) puts an early warning system in place as an effort to prepare before a disaster occurs and to mitigate floods]]>
<![CDATA[ANALISIS NILAI MARKET JAMINAN PINJAMAN DENGAN METODE MOORA ]]>
<![CDATA[Gortap Lumbantoruan]]>;
<![CDATA[Eviyanti Novita Purba]]>
<![CDATA[ METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 6 No. 2 (2022): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi ]]>
Publisher : <![CDATA[Universitas Methodist Indonesia ]]>
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DOI: 10.46880/jmika.Vol6No2.pp199-204
<![CDATA[Credit is the provision of loan money based on an agreement or loan agreement between the creditor and other parties. Credit is made after an agreement is reached between the bank or creditor and the debtor or credit recipient in which there are rights and obligations of each party. Usually banks or financial services are willing to provide loans if the debtor provides his assets as collateral or collateral to ensure the smooth running of his debt. The assessment of the guarantee or collateral must be carried out objectively and apply the precautionary principle. The estimated market value (market value) of the collateral must be based on real market data so as not to harm the creditor. To assist the lender in analyzing the market value of the collateral submitted by the debtor, the author uses the Multi-Objective Optimization On The Basis Of Ratio Analysis (MOORA) method by using several criteria including the status of documents/collateral letters, asset allocation, road conditions, locations and several other factors. From the results of the analysis using the MOORA method on the market value of the collateral submitted by the debtor, there is a difference with the market value circulating in the community. The results of this analysis can also be used as a reference in making a decision whether or not the object of the collateral submitted is appropriate to the amount of the loan being submitted. Keywords: Credit, Market Value, Collateral, MOORA.]]>
<![CDATA[ANALISIS SENTIMEN TWITTER TERHADAP WACANA PENUNDAAN PEMILU DENGAN METODE SUPPORT VECTOR MACHINE ]]>
<![CDATA[Darwis Robinson Manalu]]>;
<![CDATA[Mario Christofell L. Tobing]]>;
<![CDATA[Margaretha Yohanna]]>
<![CDATA[ METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 6 No. 2 (2022): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi ]]>
Publisher : <![CDATA[Universitas Methodist Indonesia ]]>
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DOI: 10.46880/jmika.Vol6No2.pp149-156
<![CDATA[Machine learning plays an important role in managing important issues to classify and predict information that develops ahead of the General Election in Indonesia. Especially in knowing public sentiment on the discourse on the postponement of the 2024 election through Twitter social media. So it is necessary to analyze the discourse by categorizing it as positive or negative. The Support Vector Machine (SVM) model is used for analysis and classification. The sample data used were 100 tweets data which were then scraped in the period January 2022 – May 2022. The processing was done using Python programming and Jupyter Lab tools. Before doing the analysis, do preprocess to eliminate unnecessary words and information so that the level of accuracy of the results of this Twitter sentiment classification can provide a closer picture of reality. As for the results of the grouping carried out positive sentiment in as many as 40 data tweets and negative sentiment in as many as 60 data tweets. The classification test results on tweets data with a good level of accuracy of 92%. These results are expected to be a reference for future researchers who want to improve accuracy or analysis results.]]>
<![CDATA[SIMULASI MONTE CARLO DALAM MEMPREDIKSI PEMAKAIAN OBAT PENYAKIT GIGI DAN MULUT PADA RUMAH SAKIT ]]>
<![CDATA[Resianta Perangin-angin]]>;
<![CDATA[Ika Yusnita Sari]]>;
<![CDATA[Elvika Rahmi]]>;
<![CDATA[Roni Jhonson Simamora]]>
<![CDATA[ METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 6 No. 2 (2022): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi ]]>
Publisher : <![CDATA[Universitas Methodist Indonesia ]]>
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DOI: 10.46880/jmika.Vol6No2.pp239-243
<![CDATA[The use of drugs in patients with dental disease is a necessity that needs to be considered by the hospital in providing medical services to patients. Adequate and well-managed drug supply prevents shortages or excess drug stocks. So it needs good planning in managing and monitoring drug stocks appropriately. This study aims to make predictions in the use of dental disease drugs by using a monte carlo simulation. The data used is data on the use of drugs for dental diseases from 2020 to 2022. The data on drug use processed were 12 types of drugs. The data will be processed based on the Monte Carlo simulation stages. The results of using the Monte Carlo Simulation are to obtain predictions of the use of dental disease drugs with an accuracy value reaching 89.14%. Based on the accuracy value obtained, the Monte Carlo simulation can be used to predict drug use in the future. So that the supply of dental disease medicine is maintained.]]>
<![CDATA[PENERAPAN METODE DECISION TREE C4.5 DALAM MEMPREDIKSI KELANCARAN PEMBAYARAN KREDIT DI BPR WAHANA BERSAMA KPUM ]]>
<![CDATA[Nine Situmeang]]>;
<![CDATA[Indra Kelana Jaya]]>;
<![CDATA[Margaretha Yohanna]]>
<![CDATA[ METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 6 No. 2 (2022): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi ]]>
Publisher : <![CDATA[Universitas Methodist Indonesia ]]>
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DOI: 10.46880/jmika.Vol6No2.pp215-220
<![CDATA[BPR Wahana Bersama KPUM is a lending company. Delay in credit payments is a problem that occurs in credit companies. Customers who don't pay on time can have a bad impact on their credit history. To assess customer profitability, a system is needed that can predict the smoothness of future credit payments in order to assess whether customers are profitable or not. The author uses the Decision Tree C4.5 method in which the method looks for similarities between classes or groups in the data. In this study, the data used is customer data, with training data from 2017-2020 and testing data from 2021. The existence of this system can help BPR Wahana Bersama KPUM in predicting the smoothness of credit payments in the future so that there will be no credit payment jams by customers. , this is proven by the acquisition of the accuracy value using the confusion matrix model reaching 84.18%.]]>
<![CDATA[PEMANFAATAN JARINGAN LORA UNTUK MONITORING KENDARAAN OPERASIONAL PADA INSTALASI PENELITIAN DAN PENGKAJIAN TEKNOLOGI PERTANIAN (IP2TP) KEBUN PERCOBAAN PASAR MIRING BPTP SUMATRA UTARA ]]>
<![CDATA[Ben My Cardo Simanjuntuk]]>;
<![CDATA[Fati Gratianus Nafiri Larosa]]>;
<![CDATA[Asaziduhu Gea]]>
<![CDATA[ METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 6 No. 2 (2022): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi ]]>
Publisher : <![CDATA[Universitas Methodist Indonesia ]]>
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DOI: 10.46880/jmika.Vol6No2.pp221-225
<![CDATA[Balai Pengkajian Teknologi Pertanian (BPTP) North Sumatera has an experimental garden "Pasar Miring" that is useful for academic activities, both practicum and research, installation of VUB (Varitas Unggul Baru) rice seed propagation for specific location, assessment and agricultural development. Currently official vehicles operating are 3 units, and the experimental garden area is approximately 15 hectares. Thus the administrator has difficulty knowing the position of the official vehicle during working hours due to the absence of technology that can be used by the office. So to find out the position of the official vehicle when operating, and for vehicle safety from criminal acts and also to avoid misuse of official vehicles. The propose of this paper is a monitoring system for official vehicle by using a desktop application and using Lora (Long Range) Frequency. Lora RFM95 transmitter is placed on the official vehicle and the Lora RFM95 Receiver will be set on the laptop. Lora can send the location of the official vehicle to the desktop application by Internet of Things (IoT) concept via the internet network.]]>
<![CDATA[DETERMINAN KEPUASAN DAN KINERJA PENGGUNA MODUL GLP SAKTI ]]>
<![CDATA[Hari Sugiyanto]]>;
<![CDATA[Miftahul Hadi]]>;
<![CDATA[Ria Dewi Ambarwati]]>;
<![CDATA[Anjahul Khuluq]]>
<![CDATA[ METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 6 No. 2 (2022): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi ]]>
Publisher : <![CDATA[Universitas Methodist Indonesia ]]>
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DOI: 10.46880/jmika.Vol6No2.pp205-214
<![CDATA[This study aims to analyze the factors that influence user satisfaction and net benefit (user performance) of user GLP module of SAKTI which come from system quality, information quality and service quality. Respondents are from 10 ministries/agencies that have used module GLP of SAKTI web version. Sampling is non-probability sampling (voluntary sampling). The data used came from the questionnaires filled by the respondents and obtained 49 samples. Data analysis used SEM-PLS or Structural Equation Model-Partial Least Square using SmartPLS software. The results showed that system quality, service quality have significant and positive effect on user satisfaction, but information quality has no effect on user satisfaction and user satisfaction has significant and positive effect on net benefit. Keywords: AIS, Govermental Accounting, Modul GLP]]>
<![CDATA[PEGELOLAAN DATA DAN HISTORI PENGGUNA UNTUK PENGEMBANGAN SISTEM INFORMASI BERKAH BERSAMA BERBASIS WEBISTE ]]>
<![CDATA[Sefhia Febriana Budiarti]]>;
<![CDATA[Billy Sabella]]>;
<![CDATA[Khairul Anwar Hafizd]]>
<![CDATA[ METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 6 No. 2 (2022): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi ]]>
Publisher : <![CDATA[Universitas Methodist Indonesia ]]>
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DOI: 10.46880/jmika.Vol6No2.pp226-233
<![CDATA[The Berkah Bersama Information System is an information system for manage various data of division general and finance, logistics, also production and marketing used in the Berkah Bersama Company. However, there are still some data management that are not yet included in the system and are still carried out computerized but not integrated through Microsoft Excel. Some of these data include data recording, RHPP (Daily Recapitulation of Farmer Maintenance), drug programs, chick in profit and loss, and user history. So, it is necessary to develop so that data management in the system becomes more complex. This research uses an incremental model, ERD (Entity Relationship Diagram), UML (Unified Modelling Language), and uses the CodeIgniter framework. The results of this research are in the form of Data Management and User History for the Development of a Website-Based Berkah Bersama Information System which useful for managing user data and history. System testing is carried out using the Black-Box Testing method, and the system run according to its functions.]]>
<![CDATA[TEXT MINING DAN KLASIFIKASI MULTI LABEL MENGGUNAKAN XGBOOST ]]>
<![CDATA[Rimbun Siringoringo]]>;
<![CDATA[Jamaluddin Jamaluddin]]>;
<![CDATA[Resianta Perangin-angin]]>
<![CDATA[ METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 6 No. 2 (2022): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi ]]>
Publisher : <![CDATA[Universitas Methodist Indonesia ]]>
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DOI: 10.46880/jmika.Vol6No2.pp234-238
<![CDATA[The conventional classification process is applied to find a single criterion or label. The multi-label classification process is more complex because a large number of labels results in more classes. Another aspect that must be considered in multi-label classification is the existence of mutual dependencies between data labels. In traditional binary classification, classification analysis only aims to determine the label in the text, whether positive or negative. This method is sub-optimal because the relationship between labels cannot be determined. To overcome the weaknesses of these traditional methods, multi-label classification is one of the solutions in data labeling. With multi-label text classification, it allows the existence of many labels in a document and there is a semantic correlation between these labels. This research performs multi-label classification on research article texts using the ensemble classifier approach, namely XGBoost. Classification performance evaluation is based on several metrics criteria of confusion matrix, accuracy, and f1 score. Model evaluation is also carried out by comparing the performance of XGBoost with Logistic Regression. The results of the study using the train test split and cross-validation obtained an average accuracy of training and testing for Regression Logistics of 0.81, and an average f1 score of 0.47. The average accuracy for XGBoost is 0.88, and the average f1 score is 0.78. The results show that the XGBoost classifier model can be applied to produce a good classification performance.]]>
