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All Journal Jurnal Pilar Nusa Mandiri
Hadianto, Nur
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Computer Science & IT

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IMPLEMENTASI ALGORITMA APRIORI UNTUK ANALISA PEMILIHAN TIPE GENRE FILM ANIME (STUDI KASUS : MYANIMELIST.NET) Azis, Mochammad Abdul; Hadianto, Nur; Miharja, Jaja; Rifai, Saifulloh
Jurnal Pilar Nusa Mandiri Vol 14 No 2 (2018): Pilar Nusa Mandiri : Journal of Computing and Information System Periode Septemb
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (979.116 KB) | DOI: 10.33480/pilar.v14i2.41

Abstract

As with Japanese animated films or can also be called an anime film which is now starting to be popular with all circles regardless of age, status and profession. In Japanese anime films, it has several genres from action, comedy, drama, romance, adventure, etc., can be accessed as in online media such as websites that offer various types of anime, one of which is myanimelist.net on this web having 14,000 anime films shared genre. One way to increase the appeal of anime films is to use the genre data that is often watched by anime movie lovers. With these data we foam analyze what types of genres are the most watched, as wellas the tendency to choose alternative genre types that are liked by anime movie lovers. So with these data the creator can determine the strategy for the type of genre that will be created next. a priori algorithm is good for use for itemset formation, pattern searching and so on. Therefore in this study the a priori algorithm was used to determine the pattern of selection of genre types in Japanese Anime Films.
KLASIFIKASI PEMINJAMAN NASABAH BANK MENGGUNAKAN METODE NEURAL NETWORK Hadianto, Nur; Novitasari, Hafifah Bella; Rahmawati, Ami
Jurnal Pilar Nusa Mandiri Vol 15 No 2 (2019): Pilar Nusa Mandiri : Journal of Computing and Information System Periode Septemb
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1509.803 KB) | DOI: 10.33480/pilar.v15i2.658

Abstract

Payment of loans that experience difficulties in repayment or often called bad credit is a very detrimental thing for the bank, with the occurrence of bad credit the bank does not have the maximum ability to make money for investment. Choosing the right customer must go through the right analysis because the decision to approve or disagree with the loan is the main point that determines the possibility of bad credit. This study aims to classify eligible customers to obtain loans by taking into account existing parameters such as age, total income, number of families, monthly expenditure average, education level and others. This study uses a data mining classification method with a neural network model, to assess the accuracy of data processing using rapid miners then proceed with measurements using confusion matrix, ROC curve. The results of the neural network algorithm after going through confusion matrix testing, the ROC curve shows a very high accuracy value, and the dominant value of AUC and algorithm. The accuracy value is 98.24% with AUC of 0.979
COMPARISON OF MACHINE LEARNING CLASSIFICATION ALGORITHM ON HOTEL REVIEW SENTIMENT ANALYSIS (CASE STUDY: LUMINOR HOTEL PECENONGAN) Miharja, Jaja; Putra, Jordy Lasmana; Hadianto, Nur
Jurnal Pilar Nusa Mandiri Vol 16 No 1 (2020): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (961.21 KB) | DOI: 10.33480/pilar.v16i1.1131

Abstract

Analysis of hotel review sentiment is very helpful to be used as a benchmark or reference for making hotel business decisions today. However, all the review information obtained must be processed first by using an algorithm. The purpose of this study is to compare the Classification Algorithm of Machine Learning to obtain information that has a better level of accuracy in the analysis of hotel reviews. The algorithm that will be used is k-NN (k-Nearest Neighbor) and NB (Naive Bayes). After doing the calculation, the following accuracy level is obtained: k-NN of 60,50% with an AUC value of 0.632 and NB of 85,25% with an AUC value of 0.658. These results can be determined by the right algorithm to assist in making accurate decisions by business people in the analysis of hotel reviews using the NB Algorithm.
Co-Authors Azis, Mochammad Abdul Jordy Lasmana Putra Miharja, Jaja Novitasari, Hafifah Bella Rahmawati, Ami Rifai, Saifulloh