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All Journal Dinamik Jurnal Buana Informatika CommIT (Communication & Information Technology) Jurnal Informatika Upgris Media Penelitian Pendidikan : Jurnal Penelitian dalam Bidang Pendidikan dan Pengajaran JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Teknologi Informasi dan Pendidikan Jurnal Ilmiah Sains dan Teknologi (SAINTEK) Advance Sustainable Science, Engineering and Technology (ASSET) Jurnal Teknologi dan Manajemen
Anwar, Muchamad Taufiq
Politeknik STMI Jakarta
Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Physics

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Journal : advance sustainable science engineering and technology asset

 1 
Aspect-based Sentiment Analysis on Car Reviews Using SpaCy Dependency Parsing and VADER Muchamad Taufiq Anwar; Dedy Trisanto; Ahmad Juniar; Fitra Aprilindo Sase
Advance Sustainable Science Engineering and Technology Vol 5, No 1 (2023): November-April
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v5i1.14897

Abstract

All businesses, including car manufacturers, need to understand what aspects of their products are perceived as positive and negative based on user reviews so that they can make improvements for the negative aspects and maintain the already positive aspects of their products. One of the available tools for this task is Sentiment Analysis. The traditional document-level and sentence-level sentiment analysis will only classify each document / sentence into a class. This approach is incapable of finding the more fine-grained sentiment for a specific aspect of interest, for example, comfort, price, engine, paint, etc. Therefore, in this case, Aspect-based Sentiment Analysis is used. A total of 22.702 rows of car review data are scraped from the Edmunds website (www.edmunds.com) for a specific car manufacturer. Dependency Parsing and noun phrase extraction were carried out using the SpaCy module in Python, and VADER sentiment analysis was used to determine the polarity of the sentiment for each noun phrase. Results showed that the vast majority of the sentiments are on the positive aspects: comfortable to drive, good fuel economy / mileage, reliability, spaciousness, value for money, helpful rear camera, quiet ride, good acceleration, well-designed, good sound system, and solid build. The results for the negative aspects have some similar aspects with those in the positive class but has a very low frequency. This finding means that the vast majority of the users are satisfied with multiple aspects of the produced cars. The limitation of this research and future research direction are discussed.
Fast and Accurate Indonesian QnA Chatbot Using Bag-of-Words and Deep-Learning For Car Repair Shop Customer Service Muchamad Taufiq Anwar; Azzahra Nurwanda; Fajar Rahmat; Muhammad Aufal; Hindriyanto Dwi Purnomo; Aji Supriyanto
Advance Sustainable Science Engineering and Technology Vol 5, No 2 (2023): May-July
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v5i2.14891

Abstract

A chatbot is a software that simulates human conversation through a text chat. Chatbot is a complex task and recent approaches to Indonesian chatbot have low accuracy and are slow because it needs high resources. Chatbots are expected to be fast and accurate especially in business settings so that they can increase customer satisfaction. However, the currently available approach for Indonesian chatbots only has low to medium accuracy and high response time. This research aims to build a fast and accurate chatbot by using Bag-of-Words and Deep-Learning approach applied to a car repair shop customer service. Sixteen different intents with a set of their possible queries were used as the training dataset. The approach for this chatbot is by using a text classification task where intents will be the target classes and the queries are the text to classify. The chatbot response then is based on the recognized intent. The deep learning model for the text classification was built by using Keras and the chatbot application was built using the Flask framework in Python. Results showed that the model is capable of giving 100% accuracy in predicting users’ intents so that the chatbot can give the appropriate responses and the response time is near zero milliseconds. This result implies that developers who aim to build fast and accurate chatbot software can use the combination of bag-of-words and deep-learning approaches. Several suggestions are presented to increase the probability of the chatbot’s success when released to the general public.
Aspect-based Sentiment Analysis on Electric Motorcycles: Users’ Perspective Anwar, Muchamad Taufiq; Permana, Denny Rianditha Arief; Juniar, Ahmad; Pratiwi, Anggy Eka
Advance Sustainable Science, Engineering and Technology Vol 6, No 2 (2024): February - April
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i2.18129

Abstract

Electric Vehicles (EVs) adoption is emerging especially electric motorcycles due to their lower price. Research has shown that the majority of people have positive sentiments towards EVs but most of the sentiments were from people who did not already own or use EVs, but rather from people who reacted / commented towards a product that is recently being launched/announced. This research aims to evaluate users’ opinions regarding the positive and negative aspects of electric motorcycles they had purchased / used. This information will be beneficial for the manufacturers and marketers as an evaluation for their products; and it is also beneficial for prospective buyers as a buying consideration. This research uses Aspect-Based Sentiment Analysis applied on 844 electric motorcycles review data from www.bikewale.com website. Results showed that the notable positive sentiments are related to smooth riding experience and low maintenance. Whereas notable negative sentiments are related to poor build quality and product malfunctions. The other aspects of electric motorcycles received mixed sentiments such as related to vehicle speed and customer service. The research findings, limitations, and future research direction are discussed.
Rain Prediction Using Rule-Based Machine Learning Approach Anwar, Muchamad Taufiq; Nugrohadi, Saptono; Tantriyati, Vita; Windarni, Vikky Aprelia
Advance Sustainable Science, Engineering and Technology Vol 2, No 1 (2020): November-April
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v2i1.6019

Abstract

Rain prediction is an important topic that continues to gain attention throughout the world. The rain has a big impact on various aspects of human life both socially and economically, for example in agriculture, health, transportation, etc. Rain also affects natural disasters such as landslides and floods. The various impact of rain on human life prompts us to build a model to understand and predict rain to provide early warning in various fields/needs such as agriculture, transportation, etc. This research aims to build a rain prediction model using a rule-based Machine Learning approach by utilizing historical meteorological data. The experiment using the J48 method resulted in up to 77.8% accuracy in the training model and gave accurate prediction results of 86% when tested against actual weather data in 2020.
Wildfire Risk Map Based on DBSCAN Clustering and Cluster Density Evaluation Anwar, Muchamad Taufiq; Hadikurniawati, Wiwien; Winarno, Edy; Supriyanto, Aji
Advance Sustainable Science, Engineering and Technology Vol 1, No 1 (2019): May-October
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v1i1.4876

Abstract

Wildfire risk analysis can be based on historical data of fire hotspot occurrence. Traditional wildfire risk analyses often rely on the use of administrative or grid polygons which has their own limitations. This research aims to develop a wildfire risk map by implementing DBSCAN clustering method to identify areas with wildfire risk based on historical data of wildfire hotspot occurrence points. The risk ranks for each area/cluster were then ranked/calculated based on the cluster density. The result showed that this method is capable of detecting major clusters/areas with their respective wildfire risk and that the majority of consequent fire occurrences were repeated inside the identified clusters/areas.Keywords: wildfire risk map; clustering; DBSCAN; cluster density;
Automatic Complaints Categorization Using Random Forest and Gradient Boosting Anwar, Muchamad Taufiq; Pratiwi, Anggy Eka; Udhayana, Khadijah Febriana Rukhmanti
Advance Sustainable Science, Engineering and Technology Vol 3, No 1 (2021): November-April
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v3i1.8460

Abstract

Capturing and responding to complaints from the public is an important effort to develop a good city/country. This project aims to utilize Data Mining to automatize complaints categorization. More than 35,000 complaints in Bangalore city, India, were retrieved from the “I Change My City” website (https://www.ichangemycity.com). The vector space of the complaints was created using Term Frequency–Inverse Document Frequency (TF-IDF) and the multi-class text classifications were done using Random Forest (RF) and Gradient Boosting (GB). Results showed that both RF and GB have similar performance with an accuracy of 73% on the 10-classes multi-class classification task. Result also showed that the model is highly dependent on the word usage in the complaint's description. Future research directions to increase task performance are also suggested.
Co-Authors Abas, Sidiq Waskito Ahmad Juniar Ahmad Juniar Al Kautsar Permana Azzahra Nurwanda Dedy Trisanto Denny Riandhita Arief Permana Desy Agustin Edy Winarno Fadhla Fanini Fajar Rahmat Finna Suroso Fitra Aprilindo Sase Gita Mustika Rahmah Hindriyanto Dwi Purnomo Kristophorus Hadiono Laksmi Ambarwati Lucky Heriyanto Muhammad Aufal Nugrohadi, Saptono Pratiwi, Anggy Eka Saptono Nugrohadi Sulastri Sulastri Syamsul Arifin Tantriyati, Vita Udhayana, Khadijah Febriana Rukhmanti Windarni, Vikky Aprelia Wiwien Hadikurniawati