APRIORI ALGORITHM IMPLEMENTATION TO DETERMINE PURCHASE PATTERNS OF RAW MATERIALS AT PT PENJALINDO NUSANTARA

PT. Penjalindo Nusantara is a manufacturing company in the packaging field where production depends on customer demand or what is commonly known as job orders so that timely production work and availability of sufficient materials are mandatory for the company. There was a problem in the implementation of the raw material supply strategy by PT. Penjalindo Nusantara caused delays in the supply of raw material stocks. The solution to this problem is to apply the Apriori algorithm to find out what raw materials are being purchased simultaneously so that it can be the basis for implementing a purchasing strategy in supporting the effectiveness of procurement of raw material stocks and also saving time in sending raw materials by suppliers. This research uses a Web-based data mining application to find the raw material purchase pattern. The result of this research is obtained 11 patterns of purchasing raw materials using a minimum value of 90% support and a minimum of 100% confidence with a lift ratio of 1 as a reference for determining which raw materials will be purchased at the same time.

SYSTEMATIC LITERATURE REVIEW: IMPLEMENTATION OF KNOWLEDGE MANAGEMENT IN THE ORGANIZATION

Knowledge management is an activity that organizations use to achieve goals and gain competitive advantages. This study features a systematic literature review that discusses the implementation of knowledge management in organizations covering 39 articles published from 2015 to 2020. This study aims to answer four research questions. The results show that the trend of knowledge management research in Indonesia is dominated by research related to the designing of knowledge management systems. The application of knowledge management in Indonesia has been applied in various fields. There are various models and methods that can be used in creating a knowledge management system

IMPLEMENTATION OF ENSEMBLE TECHNIQUES FOR DIARRHEA CASES CLASSIFICATION OF UNDER-FIVE CHILDREN IN INDONESIA

Diarrhea is an endemic disease in Indonesia with symptoms of three or more defecations with the consistency of liquid stool. According to WHO, diarrhea is the second largest contributor to the death of under-five children. Data and cases of children under five years who have diarrhea are very difficult to find, so the data analysis process becomes difficult due to the lack of information obtained. Difficulties in the data analysis process can be overcome by rebalancing, so the category ratios are balanced. The method that is popularly used is SMOTE. To solve imbalanced data and improve classification performance, this study implements the combination of SMOTE with several ensemble techniques in diarrhea cases of under-five children in Indonesia. Ensemble models that are used in this study are Random Forest, Adaptive Boosting, and XGBoost with Decision Tree as a baseline method. The results show that all SMOTE-based methods demonstrate a competitive performance whereas SMOTE-XGB gains a slightly higher accuracy (0.88), precision (0.96), and f1-score (0.86). The implementation of the SMOTE strategy improved the recall, precision, and f1-score metrics and give higher AUC of all methods (DT, RF, ADA, and XGB). This study is useful to solve the imbalanced problems in official statistics data provided by BPS Statistics Indonesia

NEW STUDENT ADMISSIONS INFORMATION SYSTEM WITH CLIENT SERVER BASED SMS GATEWAY

Palembang State Junior High School 58 every year accepts new students, every year SMP N 58 always uses the form in registration and collects diplomas, SKHUN, and other files in hardcopy format so that they often experience file loss for that we need a new student admission information system based client-server and SMS gateway. This information system was designed using the Software Development Life Cycle (SDLC) method, and an analysis and design were carried out using a Data Flow Diagram. This information system has the function of saving, delete, update, report automatically, and can send information in the form of SMS. The new student admission information system is user friendly so that it can be easily used, so the admission process is more effective and there are no more missing files

COMPARISON OF REGIONAL CLUSTER ANALYSIS ACCORDING TO INCLUSIVE DEVELOPMENT INDICATORS IN JAVA ISLAND 2018 BETWEEN HIERARCHICAL AND PARTITIONING CLUSTERING STRATEGIES

Gross Domestic Product (GDP) is one of the most common indicators to reflect a nation’s development. Indonesia's GDP has an average growth rate of 5 percent over the 2015-2019 period with the highest growth rate occurred in 2018. Furthermore, the provinces in Java Island contributed the most out of any province to Indonesia’s GDP in that year. However, the development in Java Island still has several issues, such as high poverty, unequal income distribution, and high unemployment. This problem indicates that the economic growth in Java Island has not been inclusive concerning development. This study aims to group regencies/municipalities in Java Island based on indicators of inclusive growth. These indicators refer to McKinley (2010) in a journal published by the Asian Development Bank (ADB). The cluster methods used to represent each hierarchical and partitioning are the Agglomerative Nesting (AGNES) and K-Means methods. The results of this study show that there are 3 clusters based on the AGNES method and 4 clusters based on the K-Means method. Clusters with good inclusive growth characteristics are dominated by municipality areas based on the K-Means method. Meanwhile, the clusters with low inclusive growth characteristics are dominated by regencies/municipalities on Madura Island based on the K-Means and AGNES methods. The comparison of the appropriate methods in this study based on the silhouette value is the AGNES method.

COMPARATIVE ANALYSIS OF SOFTWARE EFFORT ESTIMATION USING DATA MINING TECHNIQUE AND FEATURE SELECTION

Software development involves several interrelated factors that influence development efforts and productivity. Improving the estimation techniques available to project managers will facilitate more effective time and budget control in software development. Software Effort Estimation or software cost/effort estimation can help a software development company to overcome difficulties experienced in estimating software development efforts. This study aims to compare the Machine Learning method of Linear Regression (LR), Multilayer Perceptron (MLP), Radial Basis Function (RBF), and Decision Tree Random Forest (DTRF) to calculate estimated cost/effort software. Then these five approaches will be tested on a dataset of software development projects as many as 10 dataset projects. So that it can produce new knowledge about what machine learning and non-machine learning methods are the most accurate for estimating software business. As well as knowing between the selection between using Particle Swarm Optimization (PSO) for attributes selection and without PSO, which one can increase the accuracy for software business estimation. The data mining algorithm used to calculate the most optimal software effort estimate is the Linear Regression algorithm with an average RMSE value of 1603,024 for the 10 datasets tested. Then using the PSO feature selection can increase the accuracy or reduce the RMSE average value to 1552,999. The result indicates that, compared with the original regression linear model, the accuracy or error rate of software effort estimation has increased by 3.12% by applying PSO feature selection

MODEL RAD DALAM PERANCANGAN FRONT END APLIKASI PAYMENT POINT ONLINE BANK (PPOB) BERBASIS ANDROID

The conventional purchase of credit by sending sms to the agent server is often has problem. The error was caused by different format of SMS for each server. This error has an impact on the high cost of using SMS. Another problem when the customer wants to check the electricity bill, the website provided by PLN is less attractive or have to come directly to the PLN payment counter. With these problems, an Android-based application was developed to provide convenience in purchasing credit without worrying about writing format errors and checking until payment of various types of bills becomes more efficient. Bill payments become more practical in one unit, so there is no need to go to different payment counters each month and not get stuck in a long queue that wash time. Development model of this PPOB application is RAD which consists of business modeling, data modeling, process modeling, application development and testing.

PERANCANGAN NETWORK ATTACHED STORAGE (NAS) MENGGUNAKAN RASPBERRY PI UNTUK USAHA MIKRO KECIL DAN MENENGAH (UMKM)

The aim of this study is to design a network attached storage (NAS) that is easy to implement, also in terms of cost, affordable for small and medium businesses. We use Raspberry mini computers as media. Raspberry is very flexible and easy to apply for various needs, especially in the field of networking and IoT. With the Openmediavault as an Operatisng System, we success to turn external hard disks into NAS storage that can be easily configured and adapted to storage needed in an office or Small and Medium Business. Based on the evaluation results of NAS network stability with Raspberry Pi 3, showing good network stability status.

ANALISIS KINERJA ALGORITMA C4.5 DAN NAÏVE BAYES DALAM MEMPREDIKSI KEBERHASILAN SEKOLAH MENGHADAPI UN

The national exam (UN) is one of the determinants of student graduation, both elementary school, junior high school and even high school. There are many businesses that are carried out by schools to prepare their students to face national examinations. In fact almost all schools provide material deepening to their students for subjects tested at the national examination. Therefore, this study was conducted to determine the level of success of the school in preparing students in facing national examinations. The method used is a decision tree with C4.5 algorithm and naïve Bayes algorithm. From the results of the study, the results of the accuracy of the naïve bayes algorithm were as big as 95,50% , while accuracy using the c4.5 algorithm is equal to 78,50%. Then it can be concluded that the predictions generated from the naïve bayes algorithm are better compared to the c4.5 algorithm .

PREDIKSI POLA PENJUALAN TIKET KAPAL PT. PELNI CABANG MAKASSAR MENGGUNAKAN METODE ALGORITMA APRIORI

Ketersediaan tiket untuk penumpang merupakan salah satu faktor yang sangat penting dalam bidang usaha transportasi jasa angkutan kapal penumpang. Dalam menjalani proses bisnisnya, masalah yang terjadi di Cabang-Cabang PT Pelayaran Nasional Indonesia adalah menipisnya stok blanko tiket terutama pada masa peak season, sedangkan ketika Cabang membutuhkan blanko tiket tersebut, distribusi blanko tiket mengalami hambatan di bagian ekspedisi yang merupakan faktor eksternal yang tidak bisa dikontrol secara langsung. Dalam penelitian ini akan menggunakan metode apriori. Untuk melakukan proses asosiasi awalnya data mentah di preprocessing untuk memperoleh jumlah tiket yang terjual dari tiap-tiap kapal yang ada di cabang Makassar. Kemudian hasil tersebut dibagi berdasarkan periode perbulannya. Selanjutnya dilakukan dengan algoritma apriori dan terbagi menjadi 2 bagian yaitu gabungan 2 itemset dan 3-itemset yang memenuhi minimum support dan minimum confidence. Algoritma Apriori menghasilkan aturan asosiasi antar item pada bulan Januari 2018 sampai dengan Desember 2018 diketahui pola penjualan tiket kapal bahwa jika membeli tiket KM Lambelu maka akan membeli tiket KM Bukit Siguntang secara bersamaan dengan nilai support 75% dan nilai confidence 90%.