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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) IJoICT (International Journal on Information and Communication Technology) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA JSiI (Jurnal Sistem Informasi) JURIKOM (Jurnal Riset Komputer) INTEK: Jurnal Penelitian Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Data Science and Its Applications BERNAS: Jurnal Pengabdian Kepada Masyarakat eProceedings of Engineering Community Service Seminar and Community Engagement (COSECANT) Nuansa Informatika
Mahendra Dwifebri Purbolaksono
Universitas Telkom
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Neuroscience Nursing Public Health Social Sciences Veterinary Other

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Sentiment Analysis of Practo Application Reviews Using Naïve Bayes and TF-IDF Methods Rizal Adi Putranto; Mahendra Dwifebri Purbolaksono; Widi Astuti
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6311

Abstract

Entering the 4.0 era, it seems that the healthcare industry is the one most likely to benefit from the combination of physical, digital and biological systems. Digital health applications or telemedicine have experienced significant growth in recent years. In the current era, the development of telemedicine is accelerating, one of which is the Practo application. As the number of users using this app increases, it is important to get their opinions in order to improve the health services provided by the app. Therefore, sentiment analysis of the comments regarding the health services on the app is necessary to find out the users' opinions. By utilizing sentiment analysis, it is possible to use the sentiment analysis results obtained as a sample that corresponds to both positive and negative comments. In addition, it can be revealed that there is a mismatch between the ratings and comments given by users. This information has the benefit of being able to improve the Practo application and improve the health services provided to more effectively meet the needs and expectations of users. This research employs the Naïve Bayes approach for sentiment analysis, utilizing TF-IDF feature extraction. Naïve Bayes was chosen because it is known as an efficient classification algorithm but has a high level of accuracy. This approach involves utilizing the Bayes rule formula to calculate probabilities and make classifications. It is applicable for solving classification problems that involve either numeric or nominal feature data. Meanwhile, TF-IDF was chosen because it can associate each word in a document with a numerical value that reflects its level of relevance to the document. TF-IDF is used to measure the weighting of words as features in the summary. In this study, the best model achieved a performance with an f1-score of 85.50%.
Sentiment Analysis using Random Forest and Word2Vec for Indonesian Language Movie Reviews Fahriza Ichsani Rafif; Mahendra Dwifebri Purbolaksono; Widi Astuti
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6299

Abstract

The film industry in recent years has become one of the industries that people are most interested in. The convenience of watching movies through streaming services is one of the reasons why watching movies is so popular. This ease of access resulted in a large selection of available movies and encouraged the public to look for movie reviews to find out whether the movies was good or bad. Freedom of expression on the internet has resulted in many movie reviews being spread. Therefore, sentiment analysis was conducted to see the positive or negative of these reviews. The method used in this research is Random Forest and Word2Vec skip-gram as feature extraction. The Random Forest classification was chosen because Randomforest is a highly flexible and highly accurate method, while Word2Vec Skip-Gram is used as a feature extraction because it is an efficient model that studies a large number of word vectors in an irregular text. The best model obtained from this experiment is a model built with stemming, Word2Vec with 300 dimensions, and a max_depth value of 23, achieving an f1-score of 83.59%.
Handling Unbalanced Data Sets Using DBMUTE and NearMiss Methods to Improve Classification Performance of Yeast Data Sets Bima Mahardika Wirawan; Mahendra Dwifebri Purbolaksono; Fhira Nhita
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6306

Abstract

Yeast vacuole biogenesis was chosen as a model system for organelle assembly because most vacuole functions can be used for vegetative cell growth. Therefore it is possible to generate an extensive collection of mutants with defects in unbalanced vacuole assembly. With this in mind, we must find the structural balance of data in yeast. Imbalanced data is when there is an unbalanced distribution of data classes and the number of data classes is either more or lower than the number of other data classes. Our method uses the f1score performance matrix method and the balanced accuracy on DBMUTE and NearMiss undersampling. Previously, only a few studies explained the results of using a performance matrix and balanced accuracy. Then, find out the performance results of the f1 score and balanced accuracy and get the best score from the yeast datasets. In the study, a comparison between the imbalanced datasets using the undersampling method. Furthermore, to obtain the performance matrix results, use the f1 score and balance accuracy. After testing five yeast datasets, we performed an average f1 score and balance accuracy with the highest average NearMiss f1 score of 62.23% and the highest average balanced accuracy of 78.59%.
Analisis Sentimen Kategori Aspek Pada Ulasan Produk Menggunakan Metode KNN Dengan Seleksi Fitur Mutual Information Alex Wira Wilantapoera; Widi Astuti; Mahendra Dwifebri Purbolaksono
eProceedings of Engineering Vol 10, No 2 (2023): April 2023
Publisher : eProceedings of Engineering

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

Abstract

Abstrak-Analisis sentimen adalah bidang yang cukup populer untuk menganalisis opini, sikap, dan emosi terhadap suatu subjek dari banyak orang. Dalam hal ini teks ulasan menjadi alat untuk menilai, menimbang, dan mengkritik sebuah produk yang diulas. Produk adalah suatu hal yang dapat memuaskan seorang konsumen dalam bentuk yang beragam seperti barang, jasa, dan sebagainya. Pada penelitian ini dilakukan sebuah implementasi sebuah klasifikasi pada ulasan produk kecantikan dari situs Female Daily dengan menggunakan metode k-Nearest Neighbor (kNN). Metode kNN merupakan metode yang umum digunakan untuk klasifikasi. Lalu menggunakan Mutual Information (MI) sebagai metode seleksi fiturnya. Pada penelitian ini dihasilkan nilai akurasi 91,59% pada aspek price, 90,33% pada aspek packaging, 50,05% pada aspek product, dan 85,89% pada aspek aroma.Kata kunci-k-Nearest Neighbour, Ulasan Produk, Ulasan, Produk, Mutual Information, Analisis sentimen.
Perbandingan Gini Index dan Chi Square pada Sentimen Analsis Ulasan Film menggunakan Support Vector Machine Classifier Mahendra Dwifebri Purbolaksono; Deninsyah Tiya Bella Pratama; Fahmi Hamzah
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 9, No 3 (2023): Volume 9 No 3
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jp.v9i3.68845

Abstract

Pada era informasi ini semakin banyak penilaian, pendapat dan pandangan yang dapat ditemukan secara luas di dunia maya. Contohnya adalah ulasan film, di mana penonton berbagi pandangannya mengenai sebuah film. Ulasan film adalah platform di mana para penggemar film dapat mengungkapkan pendapat mereka, baik itu dalam bentuk komentar negatif atau pun positif. Sebagian besar website untuk ulasan film sudah memiliki rating atau bintang, namun rating tinggi tidak selalu diiringi oleh ulasan yang baik begitu pun sebaliknya. Untuk itu, dibutuhkan metode untuk menganalisis teks dengan tujuan mengklasifikasikan apakah ulasan film tersebut termasuk dalam kategori negatif ataupun positif. Teknik yang digunakan adalah analisis sentimen atau opinion mining. Analisis sentimen adalah bidang dalam machine learning yang bertujuan untuk mengambil informasi bersifat subjektif dari teks ulasan. Salah satu metode klasifikasi machine learning adalah Support Vector Machine (SVM). Namun semakin banyak data akan muncul beberapa masalah yaitu banyaknya kata atau fitur yang tidak relevan menyebabkan kinerja pengklasifikasian menurun. Fitur tidak relevan akan menyebab perfomansi yang rendah. Seleksi fitur Gini Indeks dan Chi-Square dibandingkan untuk mengatasi masalah kata yang tidak relevan. Pada penelitian ini, metode klasifikasi SVM kombinasikan dengan metode seleksi fitur untuk meningkatkan performansi. Kombinasi SVM dan Gini Index menghasilkan performansi F1-score sebesar 85.8%. Sedangkan menggunakan SVM dan Chi-Square menghasilkan performansi F1-score tertinggi yaitu sebesar 89.2%.
Telkom University News Topic Modeling Using Latent Semantic Analysis (LSA) Method on Online News Portal Amala, Ihsan Ahsanu; Richasdy, Donni; Purbolaksono, Mahendra Dwifebri
Building of Informatics, Technology and Science (BITS) Vol 4 No 1 (2022): June 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (415.709 KB) | DOI: 10.47065/bits.v4i1.1584

Abstract

In this day and age, the development of online news portals regarding news is quite easy to access, online news portals are information that explains an event that has occurred or is happening with electronic media intermediaries, as well as news about Telkom University which is quite easily accessible through online news portals. A system has been designed that is capable of modeling Telkom University news topics. Modeling news topics is very interesting to be used as research material because the process of understanding each individual on the topics contained in the news is different, therefore topic modeling is needed to find out what topics are news about Telkom University. In this study, a Latent Semantic Analysis (LSA) model has been designed to carry out a topic modeling process that aims to make it easier for readers to understand news topics related to Telkom University, Latent Semantic Analysis (LSA) is a mathematical method in finding hidden topics by analyzing the structure semantics of the text. After doing several research scenarios, the best coherence score was 0.524 with a total of six topics.
Sentiment Analysis on Movie Review from Rotten Tomatoes Using Modified Balanced Random Forest Method and Word2Vec Nugraha, Mohamad Rizki; Purbolaksono, Mahendra Dwifebri; Astuti, Widi
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3596

Abstract

The film industry is one of the impacts of the rapid development of technology. This causes the film industry to increase every year. In addition, technological developments also affect the public to make it easier to access various movies from various websites. With many choices of movies, people need to know the quality of various movies by knowing the reviews of these movies from other people. However, the large number of audience reviews of a movie makes it difficult for people to categorize good movies and bad movies. The solution to the problem is to perform sentiment analysis on movie reviews. In this research, the classification method used is Modified Balanced Random Forest. This method was chosen because it can overcome imbalanced data and can increase accuracy and reduce time complexity. In this research, Word2Vec is also used as feature extraction. This feature extraction was chosen because previous research explained that Word2Vec has the advantage of being able to show the contextual similarity of two words in the resulting vector. The best model produced from this research is a model built without using stemming in the preprocessing stage, using 300 dimensions in Word2Vec, and using the Modified Balanced Random Forest classification method which produces an f1-score of 84.15%.
Sentiment Analysis of Practo App Reviews using KNN and Word2Vec Farhan, Muhammad; Purbolaksono, Mahendra Dwifebri; Astuti, Widi
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3598

Abstract

The development of technology and communication is used by the community to facilitate daily activities, one of which is in the field of health services. Health services are good enough, but there are still some obstacles that are commonly found, including not allowing to leave the house or a short schedule of doctor consultations. With the presence of health service applications, one of which is Practo, it makes it easier for people to consult online. This convenience makes a lot of reviews regarding the Practo healthcare application. The diversity of opinions on the internet, makes Practo app reviews varied. Therefore, sentiment analysis of Practo app reviews is necessary. In this study, the algorithm used was KNN. The KNN algorithm was chosen because it is very effective if the amount of data is large and easy to implement. The feature extraction used in this study is Word2Vec. Word2Vec was chosen as a feature extraction because it was considered good enough to use because it represented each word with a vector. This research produced the best model built when using stemming with Word2Vec dimensions of 300 and K = 3 values on the KNN parammeter, capable of producing an f1-score of 77.30%.
Sentiment Analysis Classification on PLN Mobile Application Reviews using Random Forest Method and TF-IDF Feature Extraction Rizkiyanto, Muhamad Dafa; Purbolaksono, Mahendra Dwifebri; Astuti, Widi
INTEK: Jurnal Penelitian Vol 11 No 1 (2024): April 2024
Publisher : Politeknik Negeri Ujung Pandang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31963/intek.v11i1.4774

Abstract

PT PLN (Persero) has developed the PLN Mobile application to provide electricity services. The large number of users has resulted in various reviews regarding the strengths, weaknesses, and issues of the application. To evaluate the application's quality, sentiment analysis is conducted on user reviews. Review data is obtained through the Google-Play-Scraper API and cleaned through text preprocessing. This study utilizes the TF-IDF feature extraction method and Random Forest for classification. TF-IDF involves weighting each word in a text. This method transforms words into numerical representations, indicating both their frequency and relevance within the document's context. Random Forest is a supervised machine learning algorithm that utilizes ensemble learning which categorizes reviews into positive and negative, This study produced the best model using stemming data and TF-IDF unigram, along with a combination of hyperparameters. The n-estimator was set to 100, max_feature to log2, max_depth to unlimited (none), and entropy criterion, resulting in the highest F1-Score of up to 93.14%.
Pembangunan Aplikasi Edukasi Pendidikan Karakter Anak di SDN Palumbonsari 1 dengan Metode Gamifikasi Rizky, Achmad; Darwiyanto, Eko; Dwifebri Purbolaksono, Mahendra
eProceedings of Engineering Vol. 12 No. 1 (2025): Februari 2025
Publisher : eProceedings of Engineering

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

Abstract

Abstrak - Penelitian ini mengembangkan dan mengevaluasi aplikasi Edukasi Pendidikan Karakter Anak (AEPKA) untuk mendukung pembelajaran nilai karakter bagi siswa SD. Aplikasi ini dirancang untuk mengatasi kurangnya alat bantu pembelajaran yang interaktif dan mudah diakses, dengan menyediakan materi berupa video animasi dan game quiz. Topik ini penting karena pendidikan karakter adalah bagian esensial dari kurikulum nasional, namun alat bantu yang ada saat ini kurang mendukung keterlibatan siswa secara interaktif. Saat ini, terdapat keterbatasan pada aplikasi yang efektif menggabungkan elemen gamifikasi untuk meningkatkan minat dan keterlibatan siswa dalam pembelajaran. Solusi yang ditawarkan adalah pengembangan AEPKA, aplikasi mobile berbasis gamifikasi yang mengajarkan nilai karakter seperti kejujuran, kemandirian, dan gotong royong. Aplikasi ini diuji pada sembilan siswa melalui evaluasi efektivitas, efisiensi, dan kepuasan menggunakan metode success rate, time-based efficiency, dan System Usability Scale (SUS). Hasil evaluasi menunjukkan aplikasi ini efektif dengan success rate 91%, efisiensi waktu 19,11 detik, dan skor SUS rata-rata 86, yang masuk kategori "acceptable". Penelitian ini berkontribusi dengan menyediakan aplikasi yang efektif dan efisien dalam mendukung pendidikan karakter pada siswa SD. Kata kunci - aplikasi edukasi, pendidikan karakter, gamifikasi, usability testing, System Usability Scale (SUS)
Co-Authors Abdurrahman Al Afifi Adiwijaya Adnan Imam Hidayat Al Faraby, Said Alex Wira Wilantapoera Alfian Akbar Gozali Amala, Ihsan Ahsanu Angelia Brigitta Maharani M.P Anggitha Yohana Clara Antika Putri Permata Wardani Ardian Adam Alfarisyi Bima Mahardika Wirawan Deninsyah Tiya Bella Pratama Dody Qori Utama Donni Richasdy Eko Darwiyanto Fahmi Hamzah Fahriza Ichsani Rafif Fakhri Taufiqurrahman Felia Novitasari Fhira Nhita Firdaus, Muhammad Abdurrahman Gde Agung Brahmana Suryanegara Gede Agung Ary Wisudiawan Ghinaa Zain Nabiilah Imam Prayoga Irfan Budi Prakoso Kemas Muslim Lhaksmana Kusuma Dewi, Utami M. Mujahid Biagi Usama Mbake Woka, Adrian Yoris Muhammad Farhan Muhammad Hadiyan Wicaksono Muhammad Irvan Tantowi Naufal Hilmiaji Novia Russelia Wassi Nugraha, Mohamad Rizki Pramoedya Syachrizalhaq Lyanda Pramoedya Syachrizalhaq Lyanda Rendhy Al-Farrel Rendi Kustiawan Rizal Adi Putranto Rizki Syafaat Amardita Rizkiyanto, Muhamad Dafa RIZKY, ACHMAD Ryan Lingga Wicaksono Satria, Villy Tandya Rizky Pratama Tetuko Muhammad Hanurogo Widi Astuti Widi Astuti Winkie Setyono Yudi Priyadi