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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 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 Materials Science & Nanotechnology

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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.
Wildfire Risk Map Based on DBSCAN Clustering and Cluster Density Evaluation Muchamad Taufiq Anwar; Wiwien Hadikurniawati; Edy Winarno; Aji Supriyanto
Advance Sustainable Science, Engineering and Technology (ASSET) Vol 1, No 1 (2019)
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 Muchamad Taufiq Anwar; Anggy Eka Pratiwi; Khadijah Febriana Rukhmanti Udhayana
Advance Sustainable Science, Engineering and Technology (ASSET) Vol 3, No 1 (2021)
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.
Rain Prediction Using Rule-Based Machine Learning Approach Muchamad Taufiq Anwar; Saptono Nugrohadi; Vita Tantriyati; Vikky Aprelia Windarni
Advance Sustainable Science, Engineering and Technology (ASSET) Vol 2, No 1 (2020)
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.
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.
Co-Authors Ahmad Juniar Al Kautsar Permana Anggy Eka Pratiwi Azzahra Nurwanda Dedy Trisanto Denny Riandhita Arief Permana Desy Agustin Edy Winarno Fadhla Fanini Fajar Rahmat Fitra Aprilindo Sase Hindriyanto Dwi Purnomo Khadijah Febriana Rukhmanti Udhayana Kristophorus Hadiono Laksmi Ambarwati Lucky Heriyanto Muhammad Aufal Saptono Nugrohadi Saptono Nugrohadi Sulastri Sulastri Vikky Aprelia Windarni Vita Tantriyati Wiwien Hadikurniawati