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All Journal JOIN (Jurnal Online Informatika) JISA (Jurnal Informatika dan Sains) Intellect : Indonesian Journal of Learning and Technological Innovation
Maylawati, Dian Sa’adillah
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Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering

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Implementasi Algoritma K-Nearest Neighbor (KNN) untuk Analisis Sentimen Pengguna Aplikasi Tokopedia Lillah, M. Rival Ridautal Lillah; Maylawati, Dian Sa’adillah; Zulfikar, Wildan Budiawan; Uriawan, Wisnu; Wahana, Agung
Intellect : Indonesian Journal of Learning and Technological Innovation Vol. 2 No. 2 (2023): Intellect : Indonesian Journal of Learning and Technological Innovation
Publisher : Yayasan Lembaga Studi Makwa

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

A marketplace is a platform where sellers can come together and sell their goods or services to customers without physical meetings. In the past few decades, marketplaces have become the most popular platform for business sellers to sell their products. Becoming the number 1 marketplace in Indonesia with the most visitors on average is the right marketplace in 2023, namely Tokopedia. However, most people are skeptical of products they have never purchased or used. User reviews play an important role in product marketing, especially on Tokopedia. Reviews help potential customers build trust in the products and services offered by the seller. To analyze reviews quickly and precisely, a sentiment analysis process is needed. Natural Processing Language (NLP) and text mining algorithms are used to classify reviews as positive, or negative. One of the methods used is the K-Nearest Neighbor (KNN) algorithm, which is used to classify Tokopedia user reviews in the Play Store and App Store. The dataset consists of 1000 comment data from the Play Store and 1000 data from the App Store. A total of 2000 comments consisting of 2 labels, namely positive and negative for modeling. Meanwhile, for testing, there were 885,092 comments from the Play Store and 4000 comments from the App Store. Total 889,092 for unlabeled test data. The prediction results on the app store dataset show that there are 97.0% positive label predictions and only 3.0% negative label predictions. Abstrak Marketplace adalah platform tempat penjual dapat berkumpul dan menjual barang atau jasa mereka kepada pelanggan tanpa pertemuan fisik. Dalam beberapa dekade terakhir, pasar telah menjadi platform paling populer bagi penjual bisnis untuk menjual produk mereka. Menjadi marketplace nomor 1 di Indonesia dengan rata-rata pengunjung terbanyak adalah marketplace yang tepat di tahun 2023 yaitu Tokopedia. Namun, kebanyakan orang skeptis terhadap produk yang belum pernah mereka beli atau gunakan. Ulasan pengguna memegang peran penting dalam pemasaran produk, terutama di Tokopedia. Ulasan membantu calon pelanggan membangun kepercayaan terhadap produk dan layanan yang ditawarkan oleh penjual. Untuk menganalisis ulasan dengan cepat dan tepat, diperlukan proses analisis sentimen. Natural Processing Language (NLP) dan algoritma text mining digunakan untuk mengklasifikasikan ulasan sebagai positif, atau negatif. Salah satu metode yang digunakan adalah algoritma K-Nearest Neighbor (KNN), yang digunakan untuk mengklasifikasikan ulasan pengguna Tokopedia di play store dan app store. Dataset terdiri dari 1000 data komentar dari play store dan 1000 data dari app store. Total 2000 komentar yang terdiri dari 2 label yaitu positif dan negatif untuk pemodelan. Sedangkan untuk pengujian 885.092 komentar dari play store dan 4000 komentar dari app store. Total 889.092 untuk data pengujian yang belum dilabeli. Hasil prediksi pada dataset app store menunjukkan terdapat 97,0% prediksi label positif dan hanya 3,0% prediksi label negatif.
Implementasi Algoritma Cheapest Insertion Heuristic (CIH) dalam Penyelesaian Travelling Salesman Problem (TSP) Utomo, Rio Guntur; Maylawati, Dian Sa’adillah; Alam, Cecep Nurul
JOIN (Jurnal Online Informatika) Vol 3 No 1 (2018)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v3i1.218

Abstract

Traveling salesman problem (TSP) is the problem of a salesman to visit the city of each city connected to each other and there is the weight of travel between the cities so as to form a complete weighted graph. Departing from a certain initial city, a salesman had to visit (n-1) another city exactly once and return on the initial city of departure. The purpose of TSP is to find the route of all cities with minimum total weight.Many algorithms have been found to solve the TSP, one of which is the Cheapest Insertion Heuristic (CIH) algorithm in the process of inserting weighted steps obtained from the equation c (i, k, j) = d (i, k) + d (k, j) - d (i, j). This algorithm provides different travel routes depending on the order of insertion of cities on the subtour in question.In this final project, the writer took the problem of distribution route of mineral water of al-ma'some 240 ml cup type, with vehicle capacity to meet 1200 carton and have different customer / agent demand that is the distance of depot and agent far from each other, distribution costs.
Prediction of the COVID-19 Vaccination Target Achievement with Exponential Regression Tju, Teja Endra Eng; Maylawati, Dian Sa’adillah; Munawar, Ghifari; Utomo, Suharjanto
JISA(Jurnal Informatika dan Sains) Vol 4, No 2 (2021): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v4i2.1051

Abstract

The achievement of the national COVID-19 vaccination target in Indonesia is often reported to be uncertain with various existing obstacles. Prediction with exponential regression modeling is done by adopting part of the SKKNI Data Science with the stages of Data Understanding, Data Preparation, Modeling, Model Evaluation. The vaccination dataset from the Ministry of Health of the Republic of Indonesia for the period from January 13, 2021 to October 10, 2021, was randomly separated into training data of 0.8 parts and testing data of 0.2 parts. The optimal parameters of the exponential function are found using the scipy.optimize library in IPython. The model obtained was evaluated using MAE, RMSE, and R-Squared metrics on normalized training data, training data, test data, and recent data for seven days from 11 to 17 October 2021. The prediction results show that the vaccination target will be achieved 100 percent on January 18, 2022, while on December 31, 2021, only 80 percent will be achieved. From the recent data, it appears that more acceleration is needed, especially if it is desired to be achieved in December 2021 as determined by President Joko Widodo, there will be a shortfall of 20 percent based on the prediction results. 
Co-Authors Agung Wahana Cecep Nurul Alam, Cecep Nurul Ghifari Munawar Lillah, M. Rival Ridautal Lillah Rio Guntur Utomo Teja Endra Eng Tju Utomo, Suharjanto Wildan Budiawan Zulfikar Wisnu Uriawan, Wisnu