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Pemanfaatan E-Learning Quipper School oleh Guru dan Siswa untuk Optimalisasi Pembelajaran di MAN 1 Ponorogo Ghulam Asrofi Buntoro; Dwiyono Ariyadi; Indah Puji Astuti
Jurnal Pengabdian kepada Masyarakat (Indonesian Journal of Community Engagement) Vol 3, No 2 (2018): Maret
Publisher : Direktorat Pengabdian kepada Masyarakat Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jpkm.27404

Abstract

The role of Science and Technology (IT) is huge nowadays. The role covers many fields. One of them is education. MAN 1 Ponorogo is one of the schools in Ponorogo district that has extra computer-based activities for a minimum level of diploma 1. It affects the increase in lesson hours. Therefore, to maximize face-to-face required innovative learning media. One such example is e-learning. E-learning is also expected to be used actively by students. E-learning is a learning system that utilizes communication and information technology (ICT) in the learning process between teachers and learners. The purpose of this program is to socialize e-learning quipper to teachers and students in MAN 1 Ponorogo to optimize learning and teaching activities (KBM). In the event, seventeen teachers were present and active. Eleven teachers strongly agree with e-learning quipper because it is easy and useful. As of the 24 students present and active, 2 students still have difficulty in using e-learning quipper. Thus, more than 95% of participants can use the e-learning quipper.
Molecular Dynamics Simulations of Iron-Joining Using Copper as a Filler Metal Munaji Munaji; Ghulam Asrofi Buntoro; Agung Purniawan; Rizal Arifin
Makara Journal of Science Vol 22, No 3 (2018): September
Publisher : Directorate of Research and Community Engagement, Universitas Indonesia

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Abstract

The study of the liquid filler metal infiltration on the narrow channel of adjoining metal bears importance in understanding the mechanism of the metal brazing process. In this study, we employed the molecular dynamics simulation to understand the mechanism of Cu liquid infiltration through the narrow channel of Fe slabs. Our simulation showed that the wetting process of Fe surfaces by Cu liquid precedes the infiltration process. This study also revealed that the channel became narrower and blockages were found in the channel due to the deformation of Fe surface. In addition to the effect of viscous drag, this process should also contribute to the decreasing speed of the Cu liquid front.
ANALISIS SENTIMEN HATESPEECH PADA TWITTER DENGAN METODE NAÏVE BAYES CLASSIFIER DAN SUPPORT VECTOR MACHINE Buntoro, Ghulam Asrofi
Dinamika Informatika Vol 5, No 2 (2016): Jurnal Dinamika Informatika Volume 5 Nomor 2
Publisher : Universitas PGRI Yogyakarta

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Abstract

Today, social media, especially Twitter have enormous influence to the success or ruin the image of a person. Many movements are carried out in social media, especially Twitter, all of which can influence its success. There is a movement that aims good there is also a movement with malicious purposes, namely hatred to others. Usually the movement on Twitter was done using the hashtag (#), the latest movement there tagar Hatespeech (#HateSpeech), viewed from the name is already clear that hate speech. This study analyzes the hashtag proficiency level, all justified by the hashtag was the sentiment of hate. The classification process in this study using the method of classification Naive Bayes classifier (NBC) and Support Vector Machine (SVM) with the data preprocessing using tokenisasi, cleansing and filtering. The data used are in Indonesian tweet with the hashtag HateSpeech (#HateSpeech), with the number of datasets as much as 522 tweets were distributed evenly into two sentiments HateSpeech and GoodSpeech. The highest accuracy of results obtained when using the method of classification Support Vector Machine (SVM) with tokenisasi unigram, stopword list Indonesian and emoticons, with the average value reached 66.6% accuracy, precision value of 67.1%, 66.7% recall value TP value rate of 66.7% and 75.8% rate the value TN.
Analisis Sentimen Calon Gubernur Jawa Timur 2018 di Twitter Buntoro, Ghulam Asrofi
ScientiCO : Computer Science and Informatics Journal Vol 1, No 2 (2018): Scientico : November
Publisher : Fakultas Teknik, Universitas Tadulako

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Abstract

The East Java Governor Election 2018 is also felt in the virtual world especially Twitter. All people freely argue about their respective governor candidates, memorandum raises many opinions, not only positive or neutral also negative opinions. Media growth is so rapid, revealing a lot of online media from the news media to social media. Today's social media is not only used of friendship, but also for other activities. Promos of trading or buying and selling, until political party promos or campaigns of candidates for regents, governors, legislative candidates until presidential candidates. The research objective is to conduct a method of Sentiments Analysis for Governor candidates East Java 2018 in twitter with optimal and maximum optimization. While the benefits are to help the community conduct research on opinions on twitter which contains positive, neutral or negative sentiments. Sentiments Analysis for Governor candidates East Java 2018 in twitter using non-conventional processes that save costs, time and effort. The results of Khofifah's dataset are 77% accuracy, 79.2% precision, 77% recall, 98.6% TP rate and 22.2% TN rate. For the results of GusIpul dataset, accuracy is 76%, precision 74.4%, recall 76%, the TP rate is 93.8% and the TN rate is 52.9%.
Sentiment Analysis to Prediction DKI Jakarta Governor 2017 on Indonesian Twitter Ghulam Asrofi Buntoro
International Journal of Science, Engineering, and Information Technology Vol 2, No 02 (2018): IJSEIT Volume. 2 Issue. 2 JULY 2018
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (287.854 KB) | DOI: 10.21107/ijseit.v2i1.2744

Abstract

This study was conducted to test opinion data tweet of three candidates for governor Jakarta, 2017. Data only in Indonesian tweet, data tweet 100 tweets with keywords AHY, 100 tweets with keywords Ahok, and 100 tweets with keywords Anies. Data taken by random either from a normal user or online media at Twitter. Indonesian tweet opinion with three candidates for governor Jakarta in 2017 divided into three sentiment: positive, neutral and negative. Preprocessing data is, Lower Case Tokens, Normalization, Tokenization, Cleansing and Filtering. Classification method in this study using Naïve Bayes classifier (NBC), because this method is the most widely performed for sentiment analysis and proven always produce highest accuracy. Results of classification, Precision AHY data scored the highest with 95% and 95% Recall, while Ahok data lowest Precision scores with 81.6% and 82% recall.
SISTEM LATIH DIAGNOSA HAMA DAN PENYAKIT JAMUR TIRAM MENGGUNAKAN DECISION TREE BERBASIS ANDROID Dimas Aditya; Ghulam Asrofi Buntoro; Indah Puji Astuti
KOMPUTEK Vol 3, No 1 (2019): April
Publisher : Universitas Muhammadiyah Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (214.023 KB) | DOI: 10.24269/jkt.v3i1.229

Abstract

Kemajuan teknologi sangat berpengaruh dalam kehidupan manusia. salah satu pemanfaatan teknologi ini dirasakan para petani dalam mendapatkan informasi mengenai hama dan penyakit jamur tiram. seiring munculnya berbagai macam hama dan penyakit yang terdapat pada jamur tiram yang dipengaruhi oleh beberapa faktor utama antara lain: kondisi suhu, air, tanah, SDM (sumber daya manusia), serta bibit jamur. oleh karena itu dibutuhkan aplikasi sistem latih budidaya jamur dengan berbasis android serta mengadopsi metode decision tree serta dirancang menggunakan tool Android Studio.hasil dari perancangan aplikasi ini adalah memudahkan user dalam mendiagnosa hama dan penyakit jamur tiram tanpa harus mendatangi seorang pakar secara langsung dan bisa diakses secara langsung untuk pengguna smartphone karena berbasis android.
Analisis Sentimen Calon Gubernur DKI Jakarta 2017 Di Twitter Buntoro, Ghulam Asrofi
INTEGER: Journal of Information Technology Vol 2, No 1 (2017): Maret 2017
Publisher : Fakultas Teknologi Informasi Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (487.3 KB) | DOI: 10.31284/j.integer.2017.v2i1.95

Abstract

Abstract. Jakarta Governor Election 2017 discussed in society or internet, especially Twitter. Everyone is free opine on Jakarta governor candidate 2017 so many opinions, not only positive or neutral opinion but also negative. Social media, especially Twitter now become promotions or campaigns are effective and efficient. This research is expected be useful to conduct on public opinion containing sentiment positive, neutral or negative. The method used in this study, for data preprocessing using tokenisasi, cleansing and filtering, to define class sentiment with methods Lexicon Based. For classification using Naive Bayes classifier (NBC) and Support Vector Machine (SVM). The data is 300 tweet in Indonesian by keyword AHY, Ahok, Anies. The results of research is analysis sentiment Jakarta governor candidate 2017. The highest accuracy when using the method of classification Naïve Bayes Classifier (NBC), with average 95% accuracy, 95% precision, 95% recall, TP rate 96,8% and TN rate 84,6%.Keywords: analisis sentimen, jakarta governor candidate 2017, lexicon based, naïve bayes classifier, support vector machineAbstrak. Pemilihan Gubernur DKI Jakarta 2017 ramai diperbincangkan di dunia nyata maupun dunia maya, khususnya di media sosial Twitter. Semua orang bebas berpendapat atau beropini tentang calon Gubernur DKI Jakarta 2017 sehingga memunculkan banyak opini, tidak hanya opini yang positif atau netral tapi juga yang negatif. Media sosial khususnya Twitter sekarang ini menjadi salah satu tempat promosi atau kampanye yang efektif dan efisien. Penelitian ini diharapkan dapat bermanfaat membantu untuk melakukan riset atas opini masyarakat yang mengandung sentimen positif, netral atau negatif. Metode yang digunakan dalam penelitian ini, untuk preprocessing data menggunakan tokenisasi, cleansing dan filtering, untuk menentukan class sentimen dengan metode Lexicon Based. Untuk proses klasifikasinya menggunakan metode Naïve Bayes Classifier (NBC) dan Support Vector Machine (SVM). Data yang digunakan adalah tweet dalam bahasa Indonesia dengan kata kunci AHY, Ahok, Anies, dengan jumlah dataset sebanyak 300 tweet. Hasil dari penelitian ini adalah analisis sentimen terhadap calon gubernur DKI Jakarta 2017. Akurasi tertinggi didapat saat menggunakan metode klasifikasi Naïve Bayes Classifier (NBC), dengan nilai rata-rata akurasi mencapai 95%, nilai presisi 95%, nilai recall 95% nilai TP rate 96,8% dan nilai TN rate 84,6%.Kata Kunci: analisis sentimen, calon gubernur dki jakarta 2016, lexicon based, naïve bayes classifier, support vector machine
Sentiments Analysis for Governor of East Java 2018 in Twitter Buntoro, Ghulam Asrofi
Sinkron : Jurnal dan Penelitian Teknik Informatika Vol 3 No 2 (2019): SinkrOn Volume 3 Number 2, April 2019
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (187.882 KB) | DOI: 10.33395/sinkron.v3i2.10025

Abstract

The East Java Governor Election which will be held in 2018 is also felt in the virtual world especially Twitter social media. All people freely argue about their respective governor candidates, the memorandum raises many opinions, not only positive or neutral but also negative opinions. Media growth is so rapid, revealing a lot of online media from the news media to social media. Social media alone is Facebook, Twitter, Path, Instagram, Google+, Tumblr, Linkedin and many more. Today's social media is not only used as a means of friendship or making friends, but also for other activities. Promos of trading or buying and selling, until political party promos or campaigns of candidates for regents, governors, legislative candidates until presidential candidates. The research objective is to conduct a method of analyzing the sentiments of 2018 East Java Governor candidates on Twitter social media with optimal and maximum optimization. While the benefits are to help the community conduct research on opinions on twitter which contains positive, neutral or negative sentiments. Analysis of the sentiments of East Java Governor candidates in 2018 on twitter social media using non-conventional processes that save costs, time and effort. The results of Khofifah's dataset are 77% accuracy, 79.2% precision value, 77% recall value, 98.6% TP rate and 22.2% TN rate. For the results of Gus dataset, the accuracy is 76%, the precision value is 74.4%, the recall value is 76%, the TP rate is 93.8% and the TN rate is 52.9%.
ANALISIS SENTIMEN PENGGUNA GOPAY MENGGUNAKAN METODE LEXICON BASED DAN SUPPORT VECTOR MACHINE Rachmad Mahendrajaya; Ghulam Asrofi Buntoro; Moh Bhanu Setyawan
KOMPUTEK Vol 3, No 2 (2019): Oktober
Publisher : Universitas Muhammadiyah Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (723.682 KB) | DOI: 10.24269/jkt.v3i2.270

Abstract

Go-Pay is part of the Gojek application and one of the most popular finteches in Indonesia. Although the most popular, not all users have positive or even negative comments. Now users can submit various media opinions, one of which is Twitter. Twitter media has the advantage of a simple display, updated topics, open access to tweets and express opinions quickly. From a variety of comments on Twitter it takes a technique to divide into classes positive or negative opinions. This study uses prepocessing and labeling opinions into positive and negative classes with the lexicon Based method. As for the classification using the Support Vector Machine (SVM) method. The data used in the form of opinions about Go- Pay reviews from social media Twitter, amounting to 1210. The results of labeling with Lexicon Based amounted to 923 for positive and 287 for negative. While the classification of the SVM method using the Linear kernel produces 89.17% and 84.38% for the Polynomial kernel.
Pemanfaatan Biogas Kotoran Sapi untuk Heater Kandang Ayam Jowo Super Winangun, Kuntang; Buntoro, Ghulam Asrofi; Puspitasari, Indah; Ain, M. Fadyanto Handynur
DIKEMAS (Jurnal Pengabdian Kepada Masyarakat) Vol 3, No 2 (2019)
Publisher : Politeknik Negeri Madiun

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (303.772 KB) | DOI: 10.32486/jd.v3i2.368

Abstract

Program Kemitraan Masyarakat (PKM) yang didanai oleh Kementerian Riset, Teknologi, dan Pendidikan Tinggi melalui Direktorat Riset dan Pengabdian Masyarakat Direktorat Jenderal Riset dan Pengembangan ini bertujuan untuk pembuatan biogas dari kotoran sapi untuk pemanas kandang ayam jowo super. Hal ini dilakukan agar mitra lebih efektif dalam pemanfaatan kotoran sapi yang selama ini hanya dibuang tanpa dikelola dengan maksimal. Biogas kotoran sapi kemudian dimanfaatkan kembali untuk pemanas kandang ayam jowo super oleh mitra. Selain bantuan teknologi, juga memberi pendampingan penggunaan alat kepada masyarakat mitra agar mampu menggunakan alat tersebut. Target kegiatan Program Kemitraan Masyarakat (PKM) ini adalah pembuatan digester biogas, pembuatan pemanas untuk kandang ayam jowo super, dan publikasi media masa agar lebih dikenal masyarakat luas. Metode pendekatan yang digunakan dalam kegiatan Program Kemitraan Masyarakat (PKM) ini adalah pendidikan dan pelatihan, pendampingan, evaluasi. Teknik pelaksanaan kegiatan dilakukan dengan cara memberikan motivasi usaha, pembimbingan manajemen usaha, pelatihan keterampilan masyarakat dalam pembuatan biogas dari kotoran sapi, pelatihan penggunaan alat pemanas kandang ayam jowo super. Diharapkan kegiatan ini dapat mengatasi permasalahan mitra khususnya dalam pemanfaatan kotoran sapi dan pembuatan pemanas kandang ayam jowo super.