Around the world, the adoption of digital health applications is growing very fast. The use of e-health laboratory systems is increasing while research on the factors that impact users to use e-health laboratory systems in Indonesia has not been done much. The objective of this study is to analyze the behavioral factors of e-health laboratory users. This study includes a survey conducted on Indonesian users, and data analysis was carried out thoroughly. Based on the Technology Acceptance Model, this research framework explores a combination of variables consisting of task-driven, technology-driven, human-driven, and adoption variables to form the model proposed in this study. This model was verified using the Structural Equation Modeling (SEM) method for factor analysis, path analysis, and regression. A total of 163 respondents were collected to evaluate this research model empirically and the level of this study were individuals. These three problems are all essential in affecting usage intentions in adopting an e-health laboratory system. Specifically, task technology fit, information quality, and accessibility show a direct effect on both perceived usefulness and perceived ease of use factors perceived by the user, and have an indirect influence on the adoption of an e-health laboratory system through these two factors. The design of an online laboratory system affects perceived ease of use and personal innovativeness factors affect the perceived usefulness that users feel when adopting a laboratory system, while task technology fit and personal innovativeness factors do not affect the perceived ease of use. However, overall technology characteristic and perceived usefulness followed by design are the main predictors of adopting an e-health laboratory system on e-health systems in Indonesia.

The presence of leaf diseases in grapes can reduce the productivity of grapes and result in losses for farmers. Leaf diseases are mainly caused by bacteria, fungi, virus etc. A proper diagnosis of disease in plants is needed in order to take appropriate control measures. This paper aims to assist in the identification and classification of grape leaf diseases Convolutional Neural Network (CNN). CNN is basically an artificial neural network architecture that requires repeated training processes to get good accuracy. CNN consists of 3 stages, namely Data Input, Feature Learning, and Classification. The implementation of CNN in this study uses Keras libraries that use the python programming language. Keras is a framework created to facilitate learning of computers. The CNN training process using 0.0001 learning rate obtained results with an accuracy rate of 91,37%

The problem faced by the company is how to determine potential customers and apply CRM (Customer Relationship Management) in order to perform the right marketing strategy, so it can bring benefits to the company. This research aims to perform clustering and profiling customer by using the model of Recency Frequency and Monetary (RFM) to provide customer relationship management (CRM) recommendation to middle industrial company. The method used in this study consists of four steps: data mining from transaction history data of customer sales, data mining modeling using RFM with K-Means algorithm and customer classification with desicion tree (J48), determination of customer loyalty level and recommendation of customer relationship management (CRM) on the medium-sized industry. This research produces RFM data mining model for medium industrial companies. In addition, the results of the study provide recommendations of four customer segments and characteristics of each customer to perform customer relationship strategy.

The largest income for Southeast Asian countries comes from the export activities of wood production. The potential for timber exports in Indonesia continues to increase each year. This soaring potential needs to be continually improved by maintaining quality so that trust and good cooperation can continue to be established with partner countries. Wood quality is closely related to wood defects. The faster the detection of wood defects is, the faster the quality of the wood will be determined. The wood industry which is still manual is also very susceptible to human eye fatigue. Technology is currently developing rapidly to help human productive activities and image processing is a breakthrough to detect wood defects. This study aims to identify swietenia mahagoni wood defects using the euclidean distance method from the extraction of 6 texture and shape features GLCM (Gray Level Co-Occurance Method) including metric, eccentricity, contrast, correlation, energy, and homogeneity, which was previously segmented with the best segmentation from the comparison results of thresholding and k-means segmentation and produced an average accuracy of 95.33% with an F1 score value of 0.95. The dataset used is the primary dataset with a total of 54 images on 3 types of wood defects, namely growing skin defects on wood ends, rotten wood eye on the body, and healthy wood eye on the body. Cross validation is also applied to test the reliability of the proposed model. By using 3-fold cross validation, the optimal average accuracy is 88.90%. Validation with other similar datasets was also carried out by identifying potato leaf defects resulting in an average accuracy of 92.86% with the most optimal 3-fold cross validation value achieved an average accuracy of 83.33%. Image augmentation is also carried out in order to reproduce the image so that the reliability test of the proposed method can be carried out, namely by rotating the image 45 degrees,90 degrees,120 degrees,180 degrees which produces 84 images of augmentation, so that the total image is 138 images and gets an average accuracy from the image augmentation is 80%.

Abstrak – Kontestasi politik dalam penentuan menjadi pemimpin tingkat provinsi dalam hal ini gubernur jawa barat 2018-2023. Masyarakat yang memberikan opininya berupa tweet pada media sosial twitter menentukan bentuk dukungan atau tidaknya, sehingga perlu adanya analisis sentimen terhadap calon Gubernur agar mengetahui tingkat kepercayaan masyarakat serta terbentuk citra kepada calon Gubernur Jawa Barat 2018-2023. Akan tetapi membaca keseluruhan tweet yang tersebar dalam twitter yang berkaitan dengan masing-masing calon gubernur akan memakan waktu dan membingungkan dalam pengambilan keputusan. Klasifikasi sentimen akan mengurai masalah mengenai opini, pendapat, emosi dan prilaku dengan studi komputasi. Metode klasifikasi yang akan dibahas dalam penelitian yaitu dengan algoritma Naïve Bayes serta Support Vector Machine. Penentuan fitur menentuka hasil akurasi, dalam penentuan fitur seleksi digunakan Genetic Algorithm agar dapat meningkatan akurasi pengklasifikasian pada Support Vector Machine dan Naive Bayes. Perolehan penelitian ini yaitu klasifikasi teks dalam pola negatif atau positif dari tweet calon gubernur jawa barat 2018-2023. Pada dataset tidak seimbang Support Vector Machine menghasilkan rata-rata akurasi 92.61% dengan AUC 0,950, Naive Bayes menghasilkan rata-rata akurasi 93,29% dengan AUC 0,525, Support Vector Machine berbasis Genetic Algorithm menghasilkan rata-rata akurasi 93,03% dengan AUC 0,869, Naive Bayes berbasis Genetic Algorithm menghasilkan rata-rata akurasi 92,85% dengan AUC 0,543. Hasil ini menunjukan bahwa Support Vector Machine dapat digunakan untuk membangun deteksi tweet klasifikasi positif dan negatif dengan tingkat akurasi yang tinggi. Kebaruan dari penelitian ini adalah bahwa Support Vector Machine dapat digunakan untuk mendeteksi tweet pada dataset twitter berbahasa indonesia penulis.

The decision on financing approval in sharia cooperatives has a high risk of the inability of customers to pay their credit obligations at maturity or referred to as bad credit. To maintain and minimize risk, an accurate method is needed to determine the financing agreement. The purpose of this study is to classify sharia cooperative loan history data using the Naïve Bayes algorithm, Decision Tree and SVM to predict the credibility of future customers. The results showed the accuracy of Naïve Bayes algorithm 77.29%, Decision Tree 89.02% and the highest Support Vector Machine (SVM) 89.86%.