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Suwanto Raharjo
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INDONESIA
JNANALOKA
Published by Lentera Dua Indonesia
ISSN : 27227332     EISSN : 27222896     DOI : https://doi.org/10.36802/jnanaloka.2020.v1-no1
Core Subject : Science, Education, Social, Engineering,
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences Transportation Other
JNANALOKA merupakan jurnal ilmiah berbasis blind peer review dan open access terbit mulai tahun 2020 dipublikasikan oleh Lentera Dua Indonesia. Jurnal terbit sebanyak 2 (dua) kali dalam setahun yakni bulan Maret dan September. Redaksi Jurnal JNANALOKA menerima artikel ilmiah orisinil lintas bidang ilmu yang memiliki fokus namun tidak terbatas pada bidang sains dan teknologi baik tingkat dasar, menengah, dan tinggi lintas dan multi disiplin ilmu. JNANALOKA juga menerima artikel yang didasarkan pada penelitian ilmiah secara umum.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 5 Documents
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Search results for , issue "Vol. 03 No. 01 Maret Tahun 2022" : 5 Documents clear
Analisis Sentemen Terhadap Aplikasi Bukalapak Sebelum IPO dan Sesudah IPO Menggunakan Algoritma Naive Bayes Bayu Yanuargi
JNANALOKA Vol. 03 No. 01 Maret Tahun 2022
Publisher : Lentera Dua Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36802/jnanaloka.2022.v3-no1-17-25

Abstract

Bukalapak is one of the earliest eCommerce startups established in Indonesia. Bukalapak has been bridging between sellers (Pelapak) and buyers since 2010. In 2021 Bukalapak ventured to conduct Initial Public Offers on the IDX. There are many kinds of responses from Bukalapak users to Bukalapak's steps, both positive and negative. These negative or positive sentiments can be used as input and evaluation for Bukalapak itself to maintain the loyalty of its users. The research process starts from collecting data obtained from scrapping data on Bukalapak product reviews on Google Playstore before and after the IPO. Then preprocessing the data starting from casefolding, removing stop words, tokenization, steming to TF-IDF. The results of the preprocessing are then used as data for classification using Naive Bayes. The classification was then tested and obtained an accuracy value for the data before the IPO of 77% and the data after the IPO of 76%.
Analisis Sentimen pada review hotel menggunakan metode pembobotan dan klasifikasi Aam Munir; Enda Putri Atika; Aziza Devita Indraswari
JNANALOKA Vol. 03 No. 01 Maret Tahun 2022
Publisher : Lentera Dua Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36802/jnanaloka.2022.v3-no1-33-38

Abstract

Globally, the tourism industry has an important role in the economic progress of a region or country. This development is assisted by the development of internet technology such as social media, tourism portal websites and others. The assessment of a hotel on the portal website can also affect the consumer's desire to choose the hotel or not. Sentiment analysis on reviews issued by consumers can be divided into positive reviews or negative reviews. Sentiment analysis starts from data retrieval, namely scrapping and then proceeds to the preprocessing process so that data is obtained that is ready for analysis. After the preprocessing process is carried out, it is continued with the weighting process. the weighting process uses three methods, namely Unigram, bigram and term frequency Inverse Document frequency. After the weighting process is carried out, the classification process is carried out using two methods, namely Naive Bayes and Support vector Machine. The result of the classification process is the highest accuracy obtained by the TF-IDf weighting method and the SVM method of 95% followed by the Unigram weighting method with the SVM method of 94%.
Sentimen Twitter terhadap PILKADA kota Medan menggunakan metode Naive Bayes Prasetyo Mimboro
JNANALOKA Vol. 03 No. 01 Maret Tahun 2022
Publisher : Lentera Dua Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36802/jnanaloka.2022.v3-no1-27-32

Abstract

Indonesia is the fifth largest country with twitter users with 19.5 million users. Along with the development of information technology, twitter has become a source of information based on twitter sentiment and trending as well as the use of hashtags that are trending. Recently, the archipelago vaccine has reaped the pros and cons, to be able to classify positive and negative sentences in twitter sentiment towards the archipelago vaccine, it requires data from twitter users by taking data based on sentence classification which is then processed in the initial data before being entered into the indoBERT model which will later be resulting in the accuracy of twitter sentiment towards the archipelago vaccine. Indonesia has 19.5 million Twitter users out of a total of 500 million global users and continues to grow from time to time. Twitter users used it as an open forum for campaigns by the Medan mayoral candidate and their volunteers were asked by Netizens to respond. Netizens' responses to each tweet are both Positive and Negative. Therefore, this study tries to analyze tweets about netizens' sentiments towards the 2020 Medan City Election. Opinions or sentiments from Twitter users can of course be used as criticisms and suggestions that can be accommodated by candidates for mayor and deputy mayor of Medan. Twitter netizens often have opinions about Regional Head Candidates through their uploads. The opinions of Twitter Netizens are still random or unclassified. To facilitate the process of classifying netizen opinion data requires Sentiment Analysis. Sentiment analysis was carried out by classifying tweets containing Netizen sentiments towards the 2020 Medan City Election. The classification method used in this study is the Naive Bayes method combined with TF-IDF feature extraction. NS The validity test applied in this study used a confusion matrix. With the tf-idf extraction feature and the Naive Bayes method, it will be able to automatically classify sentiment analysis with an accuracy of 76.00%.
Analisa Perbandingan Algoritma Fuzzy Tsukamoto Dan Sugeno Untuk Menentukan Jumlah Produksi Batik Berdasarkan Data Persediaan Dan Jumlah Permintaan (Studi Kasus : Batik Jiwo Creation, Sukoharjo) Rajnaparmaitha Kusumastuti
JNANALOKA Vol. 03 No. 01 Maret Tahun 2022
Publisher : Lentera Dua Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36802/jnanaloka.2022.v3-no1-11-16

Abstract

Batik Jiwo Creation is a batik convection and sales shop that stands in the city of Sukoharjo. The amount of demand that changes every period causes uncertainty in determining the company's production amount in the coming period. Planning the number of products is very important in meeting market demand correctly and in the right amount. Analysis of determining the amount of production is carried out using the Fuzzy Tsukamoto and Sugeno Algorithm based on the amount of inventory and the number of requests. Tsukamoto and Sugeno algorithm is a method of fuzzy inference system. In the Tsukamoto method, every consequence of the if-then rule must be represented by a fuzzy set with a monotonous membership function, while the Sugeno method has the final form in the form of constants or linear equations. Based on the MAD error value on Fuzzy Tsukamoto is 17.93 while on Fuzzy Sugeno it is 210.73. This shows that the Fuzzy Tsukamoto method is better used in the calculation of production forecasting. This comparison algorithm is used to help determine the amount of production in the next period depending on the amount of demand and supply from the previous period.
Analisis Perbandingan Algoritma Nazief Adriani dan Levenshtein Distance untuk mengukur Tingkat Similaritas Berita Menggunakan Rabin Krap: Studi Kasus Berita Berbahasa Jawa Danang Kastowo; Andy Saputra; Wachid Daga Suryono; Erna Setyowati
JNANALOKA Vol. 03 No. 01 Maret Tahun 2022
Publisher : Lentera Dua Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36802/jnanaloka.2022.v3-no1-1-10

Abstract

For people in Indonesia, the regional language is the everyday language used to communicate. One of them is the Java language. In natural language-based research, regional languages are considered difficult languages to develop, given the availability of a limited number of datasets. This study analyzes 2 word stemming methods, namely the Nazief-Adriani method and the Levenshtein Distance method to carry out the Javanese word stemming process. This study wanted to find out the appropriate method with the best accuracy for stemming Javanese words. In addition, this study also considers word weighting to produce better article similarity accuracy. The nazief adriani method produces an average similarity value of 6.8% with an average execution time of 0.0443 second.

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