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All Journal Angkasa: Jurnal Ilmiah Bidang Teknologi Compiler Teknika Jurnal Nasional Teknik Elektro dan Teknologi Informasi
Hermawan Arief Putranto
Jurusan Teknologi Informasi, Politeknik Negeri Jember
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Perbandingan Metode Web Scraping Menggunakan CSS Selector dan Xpath Selector Taufiq Rizaldi; Hermawan Arief Putranto
Teknika Vol 6 No 1 (2017): November 2017
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v6i1.56

Abstract

Pemanfaatan data atau berita yang tersebar di internet untuk meningkatkan peluang keberhasilan dalam sebuah usaha melalui analisa trend pasar adalah hal yang sangat umum pada saat ini. Penjelajahan Web (Crawl) dan ekstraksi data dari web (Scraping) menjadi salah satu hal yang penting, agar tidak terjadi data yang kurang sempurna, dan data yang diterima adalah data yang paling baru. CSS Selector dan Xpath merupakan salah satu metode yang umum digunakan dalam melakukan proses crawling. Terdapat perbedaan dari jumlah data yang terambil, besar file output dan waktu pemrosesan dari kedua metode tersebut, dimana Xpath memiliki keunggulan pada jumlah data yang terambil dan waktu pemrosesnya yang berakibat pada ukuran file output yang lebih besar. Sedangkan untuk penggunaan memori pada kedua metode pada proses crawling tidak memiliki perbedaan yang signifikan.
Classification of Twitter User Sentiments Against Government Policies in Overcoming Covid-19 in Indonesia Hermawan Arief Putranto; Taufiq Rizaldi; Wahyu Kurnia Dewanto; Rokhimatus Zahro
Compiler Vol 11, No 2 (2022): November
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (834.289 KB) | DOI: 10.28989/compiler.v11i2.1286

Abstract

Sentiment classification is a field of study that analyzes a person's opinions, sentiments, judgments, evaluations, attitudes, and emotions regarding a particular topic, service, product, individual, organization, or activity. The topic that is currently being discussed is Covid-19. Covid19 is a disease caused by the corona virus, which was first identified in the city of Wuhan, China. This disease has spread throughout the world, including Indonesia. In this regard, the Indonesian government issued a policy as an effort to break the chain of the spread of the corona virus. However, this encourages the emergence of various kinds of public responses. One of them is Twitter users, there are pros and cons responses from the community in responding to government policies and causing problems, namely the difficulty of knowing positive, neutral or negative responses given by the community. Based on the explanation above, a sentiment analysis was carried out. This analysis was carried out by utilizing data from Twitter with the keywords at home, vaccines for the people of Indonesia, and PSBB, covid, covid19, covid Indonesia, vaccines Jakarta, vaccines, vaccines Restore RI, and vaccines for the sake of protecting the Republic of Indonesia. Where the data will be processed through several stages, namely preprocessing, word weighting and sentiment analysis. The results of the classification of the sentiment classification of the majority of Twitter users are neutral, namely 69.2% of the data classified as neutral sentiment, 30.1% of the data classified as positive sentiment, and 7% of the data having negative sentiment.
Pemanfaatan boundary value analysis dan equivalence partitioning pada automated testing aplikasi berbasis website Ery Setiyawan Jullev Atmadji; Ilham Robby Sanjaya; Hermawan Arief Putranto
Angkasa: Jurnal Ilmiah Bidang Teknologi Vol 15, No 1 (2023): Mei
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/angkasa.v15i1.1645

Abstract

Pengujian perangkat lunak di bidang rekayasa perangkat lunak adalah proses siklus hidup dalam proyek perangkat lunak yang memverifikasi bahwa produk atau layanan memenuhi harapan kualitas dan memvalidasi bahwa perangkat lunak memenuhi spesifikasi dan persyaratan. Pengujian perangkat lunak dimaksudkan untuk menemukan cacat pada suatu program. Semakin kompleksnya sistem perangkat lunak saat ini mengakibatkan meningkatnya kebutuhan akan teknik pengujian yang canggih. Melakukan aktivitas pengujian perangkat lunak secara manual tampaknya tidak efektif dalam hal konsumsi tenaga kerja, yang memerlukan kecepatan eksekusi yang rendah. dan cakupan tes yang tidak memadai. Untuk mengatasi permasalahan tersebut diperlukan pendekatan pengujian otomatis dengan salah satu metode yang dapat digunakan yaitu analisis nilai batas yang dipadukan dengan partisi ekivalensi, sehingga dapat membantu proses pembuatan skenario kasus uji. Inputnya kurang valid sehingga aplikasi yang dibuat masih perlu perbaikan.
Pengaruh Phrase Detection dengan POS-Tagger terhadap Akurasi Klasifikasi Sentimen menggunakan SVM Hermawan Arief Putranto; Onny Setyawati; Wijono
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 5 No 4: November 2016
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (961.687 KB)

Abstract

Sentiment analysis or opinion mining, which is one of the application of Natural Language Processing (NLP), aims to find a method to facilitate human in communicating with a computer using their common language. To simplify the process of understanding human language, there are three important stages that must be carried out by a computer, which are tokenizing, stemming and filtering. The tokenizing that breaks down the sentence into a single word will make the computer assume all words (token) are the same. If there is a phrase formed from one of unimportant words, which is happened to be in the stoplist, the phrase will be deleted. Solution for the aforementioned problem is tokenizing based on phrase detection using Hidden Markov Model (HMM) POS-Tagger to improve classification performance using Support Vector Machine (SVM).With this approach, computer will be able to distinguish a phrase from others, then store the phrase into a single entity. There is an increase in accuracy by approximately 6% on Dataset I and 3% on Dataset II in the classification process using phrase detection, due to reduction of missing features that usually occurs in the filtering process. In addition, the detection of the phrase-based approach also produces the most optimal classification model, as seen from the ROC value that reaches 0.897.
Co-Authors Ery Setiyawan Jullev Atmadji Ilham Robby Sanjaya Onny Setyawati Rokhimatus Zahro Taufiq Rizaldi Taufiq Rizaldi Wahyu Kurnia Dewanto Wijono