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All Journal Dinamik Jurnal Buana Informatika 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 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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Analisis Pola Persebaran Pornografi pada Media Sosial dengan Social Network Analysis Anwar, Muchamad Taufiq
Jurnal Buana Informatika Vol 9, No 1 (2018): Jurnal Buana Informatika Volume 9 Nomor 1 April 2018
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (685.019 KB) | DOI: 10.24002/jbi.v9i1.1667

Abstract

Abstract. The rise of social media had opened up an easy and fast way to distribute pornographic content through it. Although the negative effects linked to porn consumption are still inconclusive, government had established regulation regarding porn creation, distribution, and ownership. Unfortunately, the regulation is not well run. Porn are freely distributed through social media without any reaction from the authorities. This reseach aims to understand the distribution pattern and to find key players in the distribution of porn in social media using Social Network Analysis (SNA) so that mitigative actions could be made. Result shows that porn were first published by popular ‘Publisher’ accounts, re-shared by other publisher accounts or ‘Retweeters’, and unidirectionally consumed by followers (‘Consumers’). Interpretation and research limitations were discussed. Keywords: pornography distribution, social media, Social Network Analysis.Abstrak. Analisis Pola Persebaran Pornografi pada Media Sosial dengan Social Network Analysis. Kemunculan internet dan media sosial telah membuka cara yang mudah dan cepat untuk mendistribusikan konten pornografi. Meskipun dampak negatif yang terkait dengan konsumsi pornografi masih belum dapat disimpulkan, pemerintah telah menetapkan peraturan mengenai pembuatan, distribusi, dan kepemilikan pornografi. Sayangnya, peraturan itu tidak berjalan dengan baik. Materi pornografi didistribusikan secara bebas melalui media sosial tanpa ada reaksi dari pihak berwenang. Penelitian ini bertujuan untuk memahami pola distribusi dan menemukan pemain kunci dalam distribusi pornografi di media sosial menggunakan Social Network Analysis (SNA) sehingga tindakan mitigasi dapat dilakukan. Hasil menunjukkan bahwa film porno pertama kali diterbitkan oleh akun 'Publisher' populer, dibagikan ulang oleh akun Publisher lain atau ‘Retweeter’, dan dikonsumsi secra searah oleh pengikut (‘Consumer’). Interpretasi dan keterbatasan penelitian kemudian dibahas. Kata Kunci: distribusi pornografi, media sosial, Social Network Analysis.
ANALISIS PERBANDINGAN KLASIFIKASI PREDIKSI PENYAKIT HEPATITIS DENGAN MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBOR, NAÏVE BAYES DAN NEURAL NETWORK Sulastri Sulastri; Kristophorus Hadiono; Muchamad Taufiq Anwar
Dinamik Vol 24 No 2 (2019)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (457.739 KB) | DOI: 10.35315/dinamik.v24i2.7867

Abstract

Hepatitis merupakan penyakit yang diderita oleh banyak orang, bahkan bisa menyebabkan kematian. Prediksi awal dapat mencegah kematian tersebut yaitu denganmengumpulkan data pasien hepatitis yang dilihat dari faktor - faktornya. Faktor-faktor tersebut antara lain Protime, Alk Phosphat, Albumin, Bilirubin dan Usia. Untuk mengolah datatersebut, dibutuhkan Data Mining. Salah satu metode data mining yang digunakan pada penelitian ini adalah klasifikasi.Tujuan penelitian ini yaitu bagaimana memprediksi hidup atau meninggalnya pasien penyakit hepatitis dengan tingkat akurasi dan mencari atribut paling berpengaruh terhadapprediksi hidup atau meninggalnya pasien penyakit hepatitis dengan menggunakan algoritma Algoritma K-Nearest Neighbor, Naïve Bayes Dan Neural Network dan kemudianmembandingkan ketiga hasil analisis dari ketiga algoritma tersebut.Dari hasil analisis 20 atribut dilakukan 3 kali percobaan dengan algoritma Naïve Bayes didapat model klasifikasi dengan tingkat akurasi yang terbaik yaitu 76.92 %, tingkat error23.01% dan atribut Acites dan Spider merupakan atribut yang berpengaruh terhadap keputusan hidup atau meninggalnya pasien yang terkena penyakit hepatitis.Dengan menggunakanAlgoritma Neural Network didapat model klasifikasi dengan tingkat akurasi yang terbaik yaitu 82,97%, tingkat error 17.03% dan atribut yang paling berpengaruh yaitu anorexia, spiders dan protime. Dengan menggunakan algoritma K-Nearest Neighbor didapat model klasifikasi dengan tingkat akurasi terbaik yaitu 93%, tingkat error 7% dan atribut yang paling berpengaruh terhadap penderita penyakit hepatitis yaitu Albumin.
Analisis Pola Persebaran Pornografi pada Media Sosial dengan Social Network Analysis Muchamad Taufiq Anwar
Jurnal Buana Informatika Vol. 9 No. 1 (2018): Jurnal Buana Informatika Volume 9 Nomor 1 April 2018
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jbi.v9i1.1667

Abstract

Abstract. The rise of social media had opened up an easy and fast way to distribute pornographic content through it. Although the negative effects linked to porn consumption are still inconclusive, government had established regulation regarding porn creation, distribution, and ownership. Unfortunately, the regulation is not well run. Porn are freely distributed through social media without any reaction from the authorities. This reseach aims to understand the distribution pattern and to find key players in the distribution of porn in social media using Social Network Analysis (SNA) so that mitigative actions could be made. Result shows that porn were first published by popular ‘Publisher’ accounts, re-shared by other publisher accounts or ‘Retweeters’, and unidirectionally consumed by followers (‘Consumers’). Interpretation and research limitations were discussed. Keywords: pornography distribution, social media, Social Network Analysis.Abstrak. Analisis Pola Persebaran Pornografi pada Media Sosial dengan Social Network Analysis. Kemunculan internet dan media sosial telah membuka cara yang mudah dan cepat untuk mendistribusikan konten pornografi. Meskipun dampak negatif yang terkait dengan konsumsi pornografi masih belum dapat disimpulkan, pemerintah telah menetapkan peraturan mengenai pembuatan, distribusi, dan kepemilikan pornografi. Sayangnya, peraturan itu tidak berjalan dengan baik. Materi pornografi didistribusikan secara bebas melalui media sosial tanpa ada reaksi dari pihak berwenang. Penelitian ini bertujuan untuk memahami pola distribusi dan menemukan pemain kunci dalam distribusi pornografi di media sosial menggunakan Social Network Analysis (SNA) sehingga tindakan mitigasi dapat dilakukan. Hasil menunjukkan bahwa film porno pertama kali diterbitkan oleh akun 'Publisher' populer, dibagikan ulang oleh akun Publisher lain atau ‘Retweeter’, dan dikonsumsi secra searah oleh pengikut (‘Consumer’). Interpretasi dan keterbatasan penelitian kemudian dibahas. Kata Kunci: distribusi pornografi, media sosial, Social Network Analysis.
Model Prediksi Dropout Mahasiswa Menggunakan Teknik Data Mining Muchamad Taufiq Anwar; Lucky Heriyanto; Fadhla Fanini
Jurnal Informatika Upgris Vol 7, No 1: JUNI 2021
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/jiu.v7i1.8023

Abstract

Salah satu permasalahan yang ada di Perguruan Tinggi XYZ adalah tingginya jumlah mahasiswa yang putus studi (dropout / DO), sehingga diperlukan upaya untuk minimalisasi jumlah mahasiswa yang dropout.  Penelitian ini bertujuan untuk membangun sebuah model yang dapat memprediksi apakah seorang mahasiswa akan lulus ataukah dropout. Data diambil dari data akademis mahasiswa angkatan 2014-2019. Pemrosesan awal data dilakukan dengan Python dan pemodelan dilakukan dengan menggunakan algoritma C4.5 / J48 pada perangkat lunak WEKA (Waikato Environment for Knowledge Analysis). Hasil menunjukkan bahwa atribut yang paling menentukan apakah seorang mahasiswa DO atau lulus adalah Indeks Prestasi Semester 1 dan Indeks Prestasi Semester 2, dengan akurasi model mencapai sebesar 90.6%.
Pelatihan Assembler Edu untuk Meningkatkan Keterampilan Guru Merancang Project-based Learning Sesuai Kurikulum Merdeka Belajar Saptono Nugrohadi; Muchamad Taufiq Anwar
Media Penelitian Pendidikan : Jurnal Penelitian dalam Bidang Pendidikan dan Pengajaran Vol 16, No 1 (2022)
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/mpp.v16i1.11953

Abstract

This study aims to determine and describe: 1) student satisfaction regarding the introduction of Project Based Learning (PjBL) and Pancasila Student Profile training using Assembler Edu, 2) the relevance of PjBL introduction training and Pancasila Student Profile using Assembler Edu with the main functions of the teacher, and 3) Enter the teachers regarding the introduction of PjBL training and the Pancasila Student Profile using Assembler Edu. This research is a descriptive quantitative research. The analytical technique used is descriptive analysis using Python tools. In this study, training was held so that teachers could create projects using the Assembler Studio. The results of data analysis showed that the feedback or responses that the teachers showed related to the training carried out by the teachers were very good. the teachers are satisfied with the Assembler Edu training held by the teacher. The training that is followed can provide benefits for teachers in understanding the main tasks of teachers as students of Pancasila. In addition, the relevance of the training provided to teachers, and motivating teachers to be able to provide learning with PjBL according to the independent learning curriculum that utilizes Assembler Edu.
Perbandingan Performa Model Data Mining untuk Prediksi Dropout Mahasiwa Muchamad Taufiq Anwar; Denny Rianditha Arief Permana
Jurnal Teknologi dan Manajemen Vol. 19 No. 2 (2021): JURNAL TEKNOLOGI DAN MANAJEMEN
Publisher : Politeknik STMI Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (349.992 KB) | DOI: 10.52330/jtm.v19i2.34

Abstract

Penentuan teknik/model data mining yang tepat pada sebuah kasus sangat penting untuk mendapatkan model yang baik (tingkat akurat tinggi dan kesesuaiannya dengan masalah yang dipecahkan). Penelitian ini bertujuan untuk membandingkan performa teknik data mining untuk diterapkan pada kasus prediksi dropout mahasiswa. Perbandingan performa dilakukan menggunakan library PyCaret pada Python untuk melakukan pemodelan menggunakan 14 model / teknik data mining yaitu: Extreme Gradient Boosting, Ada Boost Classifier, Light Gradient Boosting Machine, Random Forest Classifier, Gradient Boosting Classifier, Extra Trees Classifier, Decision Tree Classifier, K Neighbors Classifier, Naive Bayes, Ridge Classifier, Linear Discriminant Analysis, Logistic Regression, SVM - Linear Kernel, dan Quadratic Discriminant Analysis. Metrik evaluasi performa model yang digunakan yaitu Accuracy, AUC, Recall, Precision, F1, Kappa, dan MCC (Matthews correlation coefficient). Hasil eksperimen menunjukkan bahwa kasus prediksi dropout mahasiswa lebih tepat jika dimodelkan dengan model berbasis ensemble learner dan pohon keputusan dengan akurasi mencapai 99%. Pohon keputusan memiliki keunggulan dibandingkan model lain seperti SVM - Linear Kernel dan Quadratic Discriminant Analysis karena ia dapat dengan lebih detil dalam memisahkan data ke dalam kedua kelas target. Setelah dilakukan penyesuaian atribut, pembuangan data dengan missing values, dan parameter tuning, didapatkan hasil akurasi yang mirip dari berbagai model yaitu sebesar 87%. Perbedaan akurasi antar model menjadi sangat kecil di saat atribut data yang digunakan sedikit.
Analisis Sentimen Masyarakat Indonesia Terhadap Produk Kendaraan Listrik Menggunakan VADER Muchamad Taufiq Anwar
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 10 No 1 (2023): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v10i1.3406

Abstract

After introducing electric vehicle products to the market, manufacturers need to find out public opinion/sentiment towards the products that have been introduced. This information can be used by manufacturers as a basis for determining the next business strategy. One technique that can be used is sentiment analysis which is part of Natural Language Processing in Data Mining / Artificial Intelligence. This study aims to determine public sentiment towards an electric vehicle product using a lexicon and rule-based sentiment analysis method approach called VADER (Valence Aware Dictionary and Sentiment Reasoner). A total of 3707 tweets (955 unique data) were taken using the tweepy library in Python and analyzed using the VADER submodule in the nltk library (Natural Language Toolkit) and visualization was made using the wordcloud library. The results showed that the majority (95%) of public sentiment was positive, and 5% negative. The positive sentiment conveyed by the public is related to product advantages such as features, design, sophistication, and environmental friendliness. Meanwhile, negative sentiments are related to the absence of fast charging and prices that are still considered uneconomical.
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.
Rancang Aplikasi Smarthome Menggunakan Rapid Application Development Berbasis Hybird Mobile Desy Agustin; Alkautsar Permana; Muchamad Taufiq Anwar; Laksmi Ambarwati
Jurnal Teknologi Informasi dan Pendidikan Vol 16 No 1 (2023): Jurnal Teknologi Informasi dan Pendidikan
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtip.v16i1.698

Abstract

Information technology and computing technology are currently being developed simultaneously to be able to provide information that is processed automatically so as to provide a system that can be used to facilitate human activities. Currently, the smart home is a technology that continues to be improved, such as speed and accuracy in the process inside. This study aims to design a Smart Home application that uses the basic concept of the Internet of Things (IOT) using the Hybrid Mobile-based Rapid application development (RAD) method. The Rapid Application Development (RAD) method focuses on developing an application quickly, precisely and adaptively. By using the RAD method, the smart home application can be applied to Hybrid Mobile where the condition of some electrical equipment in the house can be controlled remotely, namely using a smartphone application or WEB application. From the results of this study the system can monitor and control several electrical equipment at home using the button command feature on mobile and web applications.
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.
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 Fitra Aprilindo Sase 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