Techno Nusa Mandiri : Journal of Computing and Information Technology
Jurnal TECHNO Nusa Mandiri, merupakan Jurnal yang diterbitkan oleh Pusat Penelitian Pengabdian Masyarakat (PPPM) STMIK Nusa Mandiri Jakarta. Jurnal TECHNO Nusa Mandiri, berawal diperuntukan menampung paper-paper ilmiah yang dibuat oleh dosen-dosen program studi Teknik Informatika.
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<![CDATA[COMPARISON OF NAIVE BAYES ALGORITHM AND C.45 ALGORITHM IN CLASSIFICATION OF POOR COMMUNITIES RECEIVING NON CASH FOOD ASSISTANCE IN WANASARI VILLAGE KARAWANG REGENCY ]]>
<![CDATA[Yuris Alkhalifi]]>;
<![CDATA[Ainun Zumarniansyah]]>;
<![CDATA[Rian Ardianto]]>;
<![CDATA[Nila Hardi]]>;
<![CDATA[Annisa Elfina Augustia]]>
<![CDATA[ Techno Nusa Mandiri: Journal of Computing and Information Technology Vol 17 No 1 (2020): TECHNO Period of March 2020 ]]>
Publisher : <![CDATA[Lembaga Penelitian dan Pengabdian Pada Masyarakat ]]>
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DOI: 10.33480/techno.v17i1.1191
<![CDATA[Non-Cash Food Assistance or Bantuan Pangan Non-Tunai (BPNT) is food assistance from the government given to the Beneficiary Family (KPM) every month through an electronic account mechanism that is used only to buy food at the Electronic Shop Mutual Assistance Joint Business Group Hope Family Program (e-Warong KUBE PKH ) or food traders working with Bank Himbara. In its distribution, BPNT still has problems that occur that are experienced by the village apparatus especially the apparatus of Desa Wanasari on making decisions, which ones are worthy of receiving (poor) and not worthy of receiving (not poor). So one way that helps in making decisions can be done through the concept of data mining. In this study, a comparison of 2 algorithms will be carried out namely Naive Bayes Classifier and Decision Tree C.45. The total sample used is as much as 200 head of household data which will then be divided into 2 parts into validation techniques is 90% training data and 10% test data of the total sample used then the proposed model is made in the RapidMiner application and then evaluated using the Confusion Matrix table to find out the highest level of accuracy from 2 of these methods. The results in this classification indicate that the level of accuracy in the Naive Bayes Classifier method is 98.89% and the accuracy level in the Decision Tree C.45 method is 95.00%. Then the conclusion that in this study the algorithm with the highest level of accuracy is the Naive Bayes Classifier algorithm method with a difference in the accuracy rate of 3.89%.]]>
<![CDATA[THINNING STENTIFORD ALGORITHM FOR KINTAMANI INSCRIPTION IMAGE SEGMENTATION ]]>
<![CDATA[Christina Purnama Yanti]]>;
<![CDATA[I Gede Andika]]>
<![CDATA[ Techno Nusa Mandiri: Journal of Computing and Information Technology Vol 17 No 1 (2020): TECHNO Period of March 2020 ]]>
Publisher : <![CDATA[Lembaga Penelitian dan Pengabdian Pada Masyarakat ]]>
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DOI: 10.33480/techno.v17i1.1203
<![CDATA[In the copper, inscription contained writing strokes that have high historical value. Age and environmental factors cause damage to the inscription surface and also reduce the appearance of images and letters. One way to preserve it is to carry out the process of converting it into digital format. The use of the morphological operation method is very suitable to be used to improve the shape of the letters in the copper inscription. The morphological operations performed in this study were the Thinning Stentiford algorithm. Based on research that has been done, it was concluded that the Thinning Stentiford algorithm has succeeded in segmenting the letters that exist in the Kintamani copper inscription. However, there are some letters are not well segmented. This is due to the inscription background color and carved letter colors that don't have significant differences. Testing the time it was concluded that the greater the size of the image and the more letters will be segmented, the longer the processing computing.]]>
<![CDATA[DECISION SUPPORT SYSTEM FOR SELECTION OF CHAIRMAN OF OSIS USING ELECTRE METHOD IN SMK PGRI 35 WEST JAKARTA ]]>
<![CDATA[Mia Rosmiati]]>;
<![CDATA[Nur Atika Sari]]>
<![CDATA[ Techno Nusa Mandiri: Journal of Computing and Information Technology Vol 17 No 1 (2020): TECHNO Period of March 2020 ]]>
Publisher : <![CDATA[Lembaga Penelitian dan Pengabdian Pada Masyarakat ]]>
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DOI: 10.33480/techno.v17i1.1221
<![CDATA[In an organization choosing a leader who is highly dedicated, responsible and responsive to every problem is not easy. A leader is not only required to have intelligence and skill but also must have a soul of leadership, a great sense of responsibility and can be a role model. The purpose of this study was to determine the student council president using the ELECTRE method based on 4 (four) criteria, namely managerial ability, knowledge and skills, collaborative communication responsibilities and discipline. With the application of the ELECTRE method, it is expected to be able to achieve these objectives. With the implementation of the ELECTRE method in the process of electing the student council president at SMK PGRI 35 Jakarta, it can determine the student council chair with accurate results in accordance with the criteria given by the school. The results of calculations with the Electre method will obtain the highest rating, namely: A3 (Miranti Sofia) because if it indicates that the alternative is the chosen alternative.]]>
<![CDATA[COMPARISON OF DATA MINING CLASSIFICATION ALGORITHM FOR PREDICTING THE PERFORMANCE OF HIGH SCHOOL STUDENTS ]]>
<![CDATA[Tiska Pattiasina]]>;
<![CDATA[Didi Rosiyadi]]>
<![CDATA[ Techno Nusa Mandiri: Journal of Computing and Information Technology Vol 17 No 1 (2020): TECHNO Period of March 2020 ]]>
Publisher : <![CDATA[Lembaga Penelitian dan Pengabdian Pada Masyarakat ]]>
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DOI: 10.33480/techno.v17i1.1226
<![CDATA[Data Mining is a series of processes to explore added value in the form of unknown information manually from the database. In the world of data mining education can be used to obtain information about student performance. In this study the researchers took research samples from class XI (eleven) students at SMAN 3 Ambon by classifying student performance based on thirteen attributes, namely: age, sex, school organization, extracurricular activities, pocket money, duration of study at home, duration of social media, online game duration, attendance, illness, permits, semester 1 and semester 2 grades. Using the KDD (Knowledge Discovery Database) method and classification algorithm that will be used, namely, decision tree, Naïve Bayes and K-Nearest Neighbor. And then do the test using k-fold cross validation.]]>
<![CDATA[MOBILE-BASED ONLINE EXAM APPLICATIONS USING PROBLEM WEIGHT CLASSIFICATION TECHNIQUES, GROUPING AND RANDOMIZING ]]>
<![CDATA[Muhammad Iqbal]]>;
<![CDATA[Abdul Hamid]]>;
<![CDATA[Nuraeni Herlinawati]]>;
<![CDATA[Mochammad Abdul Azis]]>;
<![CDATA[Muhammad Rezki]]>;
<![CDATA[Ali Mustopa]]>
<![CDATA[ Techno Nusa Mandiri: Journal of Computing and Information Technology Vol 17 No 1 (2020): TECHNO Period of March 2020 ]]>
Publisher : <![CDATA[Lembaga Penelitian dan Pengabdian Pada Masyarakat ]]>
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DOI: 10.33480/techno.v17i1.1229
<![CDATA[Education is an agenda for designing the country's development. Implementation in the field of education is a joint responsibility of both the government and the community, educational institutions are one that plays an important role in the ongoing learning process activities one of which is the examination activities. The test is an evaluation of the learning process to obtain learning outcomes as a form of achievement recognition or completion in an educational unit. The test is still cheating, it is triggered by the lack of confidence in working on the exam questions and the same type of exam questions will provide an opportunity to chat and work together. The author aims to provide a solution in the form of the application of online-based online test applications using question weight classification techniques, grouping and randomization. This mobile-based online exam application was developed using the waterfall model. The results obtained from research on this mobile-based exam application has features to prevent screen capture or screenshots, prevent video recording or video recorder and prevent switching applications that can run multiplatform on Android and iOS. This application has been through the process of testing the user and distributing questionnaires to determine the feasibility of using the weight classification technique with a percentage of 80% so it is suitable for use in examination activities.]]>
<![CDATA[NAIVE BAYES ALGORITHM IMPLEMENTATION TO DETECT HUMAN PERSONALITY DISORDERS ]]>
<![CDATA[Yoga Aditama Ika Nanda]]>;
<![CDATA[Bety Wulan Sari]]>
<![CDATA[ Techno Nusa Mandiri: Journal of Computing and Information Technology Vol 17 No 1 (2020): TECHNO Period of March 2020 ]]>
Publisher : <![CDATA[Lembaga Penelitian dan Pengabdian Pada Masyarakat ]]>
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DOI: 10.33480/techno.v17i1.1239
<![CDATA[We live in a society that still sees problems regarding one's soul and personality as taboo, even though mental health is as important as physical health. A personality disorder itself is a disorder that can be seen from behavior, mindset, and attitude, which brings difficulties to life. Based on this problem, this study applies the method of Naive Bayes classifier as early detection of human personality disorders. Using a data set of 130 correspondences from the AMIKOM university scope with the age limit of 18-25 years and identified personality disorders is a borderline type disorder. The data obtained was 94 with undiagnosed classes and 36 with undiagnosed classes, with the research variables in the form of questionnaire questions as many as 13 questions. The testing process is done with 10 fold and 5 fold cross-validation, and confusion matrix with the results in the form of accurate 10 folds superior with a value of 88.8% compared to 5 folds that is 88.2%, for precision 10 folds superior with 88.7%, but for 5 fold recall superior with 88.3%, while the final results of these two performances in F1-Score, produce the same value, which is 86.1%.]]>
<![CDATA[DATA MINING FOR PREDICTING THE AMOUNT OF COFFEE PRODUCTION USING CRISP-DM METHOD ]]>
<![CDATA[Ali Khumaidi]]>
<![CDATA[ Techno Nusa Mandiri: Journal of Computing and Information Technology Vol 17 No 1 (2020): TECHNO Period of March 2020 ]]>
Publisher : <![CDATA[Lembaga Penelitian dan Pengabdian Pada Masyarakat ]]>
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DOI: 10.33480/techno.v17i1.1240
<![CDATA[The production of coffee plantations has become the leading plantation commodity with the export value of the fourth rank after oil palm, rubber and coconut. The number of coffee needs for export every year always increases, therefore it is necessary to predict the yield of coffee plants to estimate planting and anticipation that will be done so as to achieve the target. Coffee plant productivity is influenced by internal and external factors, namely the quality of the plant itself, soil, altitude and climate. The method used in this study is the CRISP-DM method and multiple linear regression algorithm to predict the amount of coffee production and determine the relationship between the variables. The steps taken are business understanding, data understanding, data preparation, modeling and evaluation. The data set that is used as many as 170 data after going through the data preparation stage produced 150 data with 5 attributes in the table. With calculations using tools, the coefficient of determination is 91.96%. That the variation in the value of the production of coffee plants is influenced by independent variables, namely the area of plantations, rainfall, air pressure and solar radiation by 91.96% and 8.04% influenced by other variables not measured in this study. The results of the evaluation and validation of predictions produce good accuracy with an RMSE value of 0.3477.]]>
<![CDATA[IDENTIFICATION OF HERBAL PLANT BASED ON LEAF IMAGE USING GLCM FEATURE AND K-MEANS ]]>
<![CDATA[Recha Abriana Anggraini]]>;
<![CDATA[Fanny Fatma Wati]]>;
<![CDATA[Muhammad Ja’far Shidiq]]>;
<![CDATA[Ade Suryadi]]>;
<![CDATA[Haerul Fatah]]>;
<![CDATA[Desiana Nur Kholifah]]>
<![CDATA[ Techno Nusa Mandiri: Journal of Computing and Information Technology Vol 17 No 1 (2020): TECHNO Period of March 2020 ]]>
Publisher : <![CDATA[Lembaga Penelitian dan Pengabdian Pada Masyarakat ]]>
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DOI: 10.33480/techno.v17i1.1087
<![CDATA[Medicinal plants are one of the groups of plants that have enormous benefits for humans because they can help the medical process for healing disease. Herbal plants can be used as ingredients for medicines, medicines produced from herbal plants are also natural. Lack of knowledge of herbal plants causes people to prefer chemical-based medicines to help cure their diseases, even though chemical-based drugs have side effects on human health. This study aims to identify types of herbal plants based on the extraction of contrast, correlation, energy, and homogeneity features as well as shape recognition based on metric and eccentricity values. The method used in this research is GLCM features and K-means clustering. In this study, the data used consisted of 352 data divided into 320 training data and 32 testing data. This research succeeded in identifying and classifying herbal plant species using GLCM features and K-means clustering segmentation with an average accuracy value of 85.94%.]]>
<![CDATA[ANALYSIS OF NEURAL NETWORK CLASSIFICATION ALGORITHM TO KNOW THE SUCCESS LEVEL OF IMMUNOTHERAPY ]]>
<![CDATA[Agung Fazriansyah]]>;
<![CDATA[Mochammad Abdul Azis]]>;
<![CDATA[Yudhistira Yudhistira]]>
<![CDATA[ Techno Nusa Mandiri: Journal of Computing and Information Technology Vol 17 No 1 (2020): TECHNO Period of March 2020 ]]>
Publisher : <![CDATA[Lembaga Penelitian dan Pengabdian Pada Masyarakat ]]>
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DOI: 10.33480/techno.v17i1.1089
<![CDATA[Cancer is a disease that is feared by humans at this stage, the genetic term of most diseases that have the characteristics of abnormal cell growth and beyond the normal cell limits so that they can attack cells that cover and are able to spread to other organs. For cancer recovery therapy is immunization therapy. Of course in this alternative treatment still needs to be done research to determine the level of success with existing conditions and parameters. Increasingly sophisticated, developing technology that helps human work. The neural network algorithm is used to analyze large datasets, the purpose of this study is to find the accuracy and immunotherapy methods of the dataset using a neural network learning machine with 200 data training cycles, 0.9 momentum and 0.01 learning levels that produce quite high accuracy 80 % and AUC value of 0.738]]>
<![CDATA[IMPLEMENTATION OF DECISION TREE AND K-NN CLASSIFICATION OF INTEREST IN CONTINUING STUDENT SCHOOL ]]>
<![CDATA[Daniati Uki Eka Saputri]]>;
<![CDATA[Fitra Septia Nugraha]]>;
<![CDATA[Taopik Hidayat]]>;
<![CDATA[Abdul Latif]]>;
<![CDATA[Ade Suryadi]]>;
<![CDATA[Achmad Baroqah Pohan]]>
<![CDATA[ Techno Nusa Mandiri: Journal of Computing and Information Technology Vol 17 No 1 (2020): TECHNO Period of March 2020 ]]>
Publisher : <![CDATA[Lembaga Penelitian dan Pengabdian Pada Masyarakat ]]>
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DOI: 10.33480/techno.v17i1.1289
<![CDATA[Education is important to prepare quality Human Resources (HR) because quality human resources is an important factor for the nation and state development. Therefore, it is expected that every citizen has the right to get high educational opportunities from the 12-year compulsory education level. This study aims to implement the Decision Tree and K-NN algorithm in the classification of student interest in continuing school. This study proposes combining the Decision Tree and K-NN algorithm methods to improve accuracy with the Gain Ratio, Information Gain and Gini Index approaches for the measurement process. The test results show that the use of the Decision Tree algorithm produces an accuracy value of 97.30% while using the K-NN algorithm produces an accuracy of 89.60%. While the proposed method by combining the Decision Tree and K-NN algorithms produces an accuracy value of 98.07%. The results of evaluation measurements using the Area Under Curve (AUC) on the Decision Tree algorithm are 0.992 and the AUC on K-NN is 0.958 and on the combination of the Decision Tree and K-NN algorithms of 0.979. These results indicate that the proposed algorithm is very significant towards increasing accuracy in the classification of the interests of high school students continuing school]]>
