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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Journal of Information Systems Engineering and Business Intelligence Register: Jurnal Ilmiah Teknologi Sistem Informasi Indonesia Symposium on Computing Indonesian Journal on Computing (Indo-JC) IJoICT (International Journal on Information and Communication Technology) JOIN (Jurnal Online Informatika) Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Seminar Nasional Teknologi Informasi Komunikasi dan Industri Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Journal of Computer Science and Informatics Engineering (J-Cosine) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Building of Informatics, Technology and Science Journal of Computer System and Informatics (JoSYC) Journal of Applied Data Sciences Charity : Jurnal Pengabdian Masyarakat Indonesian Community Journal Jurnal Pengabdian Masyarakat Akademisi eProceedings of Engineering Community Service Seminar and Community Engagement (COSECANT) LOGIC: Jurnal Penelitian Informatika
Ade Romadhony
Universitas Telkom
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Public Health Social Sciences Other

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Analisis Sentimen Masyarakat Terhadap Hasil Kerja Petahana Dalam Kaitan Dengan Pemilihan Presiden Tahun 2019 Pada Sosial Media Twitter Menggunakan Support Vector Machine (svm) Ridea Valentini Peristiwari Siwabessy; Anisa Herdiani; Ade Romadhony
eProceedings of Engineering Vol 6, No 2 (2019): Agustus 2019
Publisher : eProceedings of Engineering

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

Abstract

AbstrakCalon presiden dan wakil presiden pada pemilihan umum tahun 2019, terdiri dari petahana dan salah satucalon presiden yang baru. Petahana telah bekerja selama 4 tahun untuk membangun Indonesia. Dalammasa kepemimpinannya banyak program kerja yang telah dikerjakannya. Berbagai penilaian terhadaphasil kerjanya bermunculan. Ada yang pro, tetapi juga ada yang kontra dengan hasil kerja petahana.Sosial media Twitter merupakan salah satu platform yang sering digunakan untuk menyampaikanberbagai penilaian terhadap hasil kerja petahana. Informasi yang terdapat pada Twitter berupa pertanyaan,opiniataukomentar,baikyangbersifatpositifmaupunnegatif.Setiaptweetyangmenyatakanapresiasimaupunpenolakanmerupakanbentukekspresidarimasyarakatsebagairesponterhadaphasilkerjapetahana.Dalampenelitianini,dibangunsebuahsistemyangdapatmengklasifikasikantweetberdasarkansentimentmasyarakatterhadaphasilkerjasangpetahanaberdasarkantweet.UntukmengklasifikasikansentimenberdasarkanparameternyadigunakanmetodeSupportVectorMachine(SVM)sebagaiclassifiernya.Hasilyangdidapatkanbahwaskenario3(kombinasiTF-IDF+Stemming)danskenario8(kombinasiWordCount+Stemming)memilikiakurasibaikyaitu81,58%dan77,56%. Katakunci:supportvectormachine,sentimen,twitter,pilpres  AbstractPresidentialandvicepresidentialcandidatesinthe2019generalelection,consistingofincumbentandoneofthenewpresidentialcandidates.Incumbenthasworkedfor4yearstodevelopIndonesia.Inhisleadershipprogrammanyworkprogramshehasdone.Variousconsiderationsontheresultsofdiscussionsemerged.Therearepros,buttherearealsoconswiththeworkofincumbents.SocialmediaTwitterisoneofplatformthatisoftenusedtopresentvariousassessmentsofincumbent'swork.InformationsuggestedonTwitterincludesquestions,opinionsorcomments,bothpositiveandnegative.Everytweetthatexpressesappreciationisalsoaformofresponsefromthecommunityinresponsetotheincumbent'swork.Inthisstudy,asystemwasbuiltthatcouldclassifytweetsbasedoncommunitysentimenttowardstheincumbent'sworkbasedon tweets. To classify sentiments based on their parameters the Support Vector Machine (SVM)method is used as the classifier. The results obtained were scenario 3 (TF-IDF + Stemming combination)and scenario 6 (Word Count + Stemming combination) have good accuracy that is 81,58% and 77,56%.Keywords: support vector machine, sentiment, twitter, general election
ANALISIS KORELASI NILAI MICROTEACHING GURU DENGAN KEMAMPUAN PEMBUATAN SOAL YANG MENGINTEGRASIKAN BERPIKIR KOMPUTASIONAL PADA MATA PELAJARAN MELALUI GERAKAN PANDAI Muhammad Arzaki; Ade Romadhony; Putu Harry Gunawan; Rimba Whidiana Ciptasari; Fazmah Arif Yulianto; Selly Meliana; Agung Toto Wibowo; Bambang Pudjoatmodjo; Dodi Wisaksono Sudiharto; Fat’hah Noor Prawita; Ema Rachmawati
Prosiding COSECANT : Community Service and Engagement Seminar Vol 1, No 2 (2021)
Publisher : Universitas telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (432.678 KB) | DOI: 10.25124/cosecant.v1i2.17506

Abstract

Berpikir Komputasional (BK) merupakan metode berpikir secara sistematis dan logis dalam menyelesaikan suatu masalah. Biro Bebras Universitas Telkom menyelenggarakan lokakarya dengan tujuan melatih guru untuk mengintegrasikan BK ke dalam mata pelajaran pada rumpun STEM maupun non-STEM untuk tingkat SD, SMP dan SMA. Pada lokakarya ini, terdapat 146 peserta guru yang setengahnya merupakan guru SMA atau sederajat dan lebih dari seperempatnya adalah guru SD atau sederajat. Dalam kegiatan lokakarya, guru-guru diberikan pelatihan BK, membuat rencana pembelajaran (RP), membuat deskripsi soal (DS) dan melaksanakan microteaching (MT) sebagai penerapan dari kegiatan lokakarya. Dari hasil analisis data yang dilakukan, nilai korelasi antara nilai pembuatan DS dan aktivitas MT secara keseluruhan adalah 0,08151 dari total 33 peserta yang mengikuti serangkaian tugas DS dan MT. Sehingga, secara statistik dapat disimpulkan bahwa tidak ada pengaruh signifikan antara aktivitas pembuatan DS dengan aktivitas MT pada kegiatan lokakarya ini.
KORELASI ANTARA NILAI LATIHAN SOAL BERPIKIR KOMPUTASIONAL DAN HASIL TANTANGAN BEBRAS PADA SISWA SEBAGAI BAGIAN DARI PENINGKATAN KESIAPAN GURU DALAM GERAKAN PANDAI Muhammad Arzaki; Ema Rachmawati; Ade Romadhony; Bambang Pudjotatmodjo; Dodi Wisaksono Sudiharto; Putu Harry Gunawan; Agung Toto Wibowo; Selly Meliana; Rimba Whidiana Ciptasari; Fazmah Arif Yulianto; Fat’hah Noor Prawira; Bedy Purnama
Prosiding COSECANT : Community Service and Engagement Seminar Vol 2, No 1 (2022)
Publisher : Universitas telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (543.859 KB) | DOI: 10.25124/cosecant.v2i1.18436

Abstract

Pelatihan Berpikir Komputasional untuk Peningkatan Kompetensi Guru Telkom Schools sebagai Bagian dari Gerakan PANDAI Muhammad Arzaki; Selly Meliana; Ema Rachmawati; Ade Romadhony; Agung Toto Wibowo; Bambang Pudjoatmodjo; Bedy Purnama; Dodi Wisaksono Sudiharto; Fat'hah Noor Prawira; Fazmah Arif Yulianto; Putu Harry Gunawan; Rimba Whidiana Ciptasari
I-Com: Indonesian Community Journal Vol 3 No 3 (2023): I-Com: Indonesian Community Journal (September 2023)
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33379/icom.v3i3.2988

Abstract

Berpikir komputasional (BK) atau computational thinking (CT) merupakan salah satu keahlian esensial yang diperlukan sumber daya manusia Indonesia dalam rangka menghadapi revolusi industri 4.0 dan masyarakat 5.0. Gerakan PANDAI (Pengajar Era Digital Indonesia) merupakan suatu gerakan nasional yang merupakan kolaborasi nirlaba antara komunitas Bebras Indonesia, Kementerian Pendidikan dan Kebudayaan Indonesia, dan Google Indonesia dalam rangka meningkatkan kompetensi BK yang dimiliki oleh guru sekolah dasar dan menengah. Pada tahun 2022, Biro Bebras Universitas Telkom mengadakan pelatihan BK kepada lebih dari 60 guru Telkom Schools sebagai bagian dari gerakan ini. Pelatihan ini terdiri dari lima tahapan besar yang meliputi lokakarya luring, pembelajaran mandiri, lokakarya daring, dan dua kegiatan microteaching. Hasil analisis kuantitatif menunjukkan peningkatan kemampuan konseptual peserta terkait BK, meskipun masih banyak hal yang perlu dibenahi dari sisi kemampuan teknis dalam pengerjaan soal-soal BK.
Similar Questions Identification on Indonesian Language Subject Using Machine Learning Hasmawati; Ade Romadhony
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 12 No. 2 (2023)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v12i2.62582

Abstract

Question similarity is carried out to evaluate similarities between questions in a collection of questions in the question and answer forum and on other platforms. This is done to improve the performance of the question-and-answer forum so that new questions submitted by users can be identified as similar to existing questions in the database. Currently, research related to question similarity is still being carried out on foreign language datasets. The purpose of this research is to identify the similarity of questions in a collection of questions in Indonesian. The method used is Support Vector Machine and IndoBERT. For feature extraction, we evaluate the lexical features and syntax features of each question. For lexical feature extraction, we use the cosine similarity algorithm to calculate the distance between two objects which are represented as vectors. For syntax feature extraction we use the Indonesian part of speech tagger (POS Tag). The dataset used is a collection of questions on Indonesian subjects at the primary and secondary school levels. The results of this study show that the best performance of the Support Vector Machine is obtained from the use of the cosine similarity feature with an accuracy of 85%. While the use of the POS Tag feature or the combination of POS Tag and cosine similarity causes the model to be overfitted and the accuracy decreases to 77%. Meanwhile, for the IndoBERT model, an accuracy of 95% was obtained. 
Identifikasi Kesamaan Pertanyaan pada Soal Bahasa Indonesia Menggunakan Metode Recurrent Neural Network (RNN) Muhammad Iqbal; Hasmawati; Ade Romadhony
JOIN (Jurnal Online Informatika) Vol 8 No 2 (2023)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v8i2.1138

Abstract

In a question-and-answer forum, the identification of question similarity is used to determine how similar two questions are. This procedure makes sure that user-submitted questions are compared to the questions in a database for matches to improve system performance on the online Q&A platform. Currently, question similarity is mostly done in foreign languages. The purpose of this research is to identify question similarities and evaluate the effectiveness of the methods used in Indonesian language questions. The data used is a public dataset with labeled pairs of questions as 0 and 1 where label 0 for different pairs of questions and label 1 for the same pairs of questions. The method used is a Recurrent Neural Network (RNN) with the Manhattan Distance approach to calculate the similarity distance between two questions. The question pairs are taken as two inputs with a reference label to identify the similarity distance between the two question inputs. We evaluated the model using three different optimizers namely RMSprop, Adam, and Adagrad. The best results were obtained using the Adam optimizer with 80:20 ratio split-data and overall accuracy is 76%, precision is 74%, recall is 98.8%, and F1-score is 85.1%.
Difficulty Level Identification of Indonesian and Mathematics Multiple Choice Questions using Machine Learning Approach Ningsih, Shabrina Retno; Romadhony, Ade
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.v5i1.3649

Abstract

Examination question design is an important factor that could improve education, which could help teachers to analyze student understandings. Designing question should consider difficulty level, which commonly classified into three types: easy, medium, difficult. Predicting the difficulty level of questions is very important to help teachers form questions and know the level of student ability. In this study, we tackle question difficulty level identification as a classification problem. We use a dataset of Indonesian and mathematic question from elementary and junior or school exercise questions set and employ several machine learning methods on classification. We use Random Forest, Logistic Regression, SVM, Gaussian, and Dense NN on the experiment, with embeddings, lexical, and syntactic feature. The evaluation result shows that the best method on identifying question difficult level on Indonesian subject is Random Forest with 83% accuracy, while on mathematic subject the best method is Random Forest with 83% accuracy. Result analysis shows that embedding feature affect the model accuracy.
Paraphrase Generation For Reading Comprehension Januarahman, Faishal; Romadhony, Ade
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.12873

Abstract

Reading comprehension is an assessment that tests readers understanding of a concept from the given text. The testing process is conducted by providing questions related to the content within the context of the text. The purpose of this research is to create new question variations from existing questions, and one of the methods to achieve this is by paraphrasing questions through the task of paraphrase generation. This can help ensure that readers have fully grasped a concept of a text. This study employs a traditional approach known as the thesaurus-based approach, in which the process involves substituting synonyms using the Indonesian Thesaurus dictionary. The data used consists of a list of Indonesian language reading comprehension assessment questions ranging from elementary to high school levels. To measure the quality of the generated paraphrased questions, two evaluation processes are conducted which are automatic evaluation with the scores ranging from 0-1 and human evaluation with score ranging from 1-4. The automatic evaluation includes the BLEU-4 metric, resulting in a score of 0.044, and the ROUGE-L metric, resulting an F1-score of 0.421. As for human evaluation, the obtained relevancy score is 2.533, and the fluency score is 3.186. The results from both evaluation metrics indicate that the generated paraphrased questions exhibit diverse new word choices but tend to have slightly different meanings compared to the reference questions.
Identification of 10 Regional Indonesian Languages Using Machine Learning Nugraha, Azhar Baihaqi; Ade Romadhony
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.12989

Abstract

Language Identification plays a pivotal role in deciphering the rich tapestry of Indonesia's diverse regional languages, encompassing a wide spectrum of scripts, and spoken forms. Language Identification, an integral component of Natural Language Processing, is frequently addressed through Text Classification. In this study, we embark on the task of identifying 10 Indonesian languages, leveraging the NusaX dataset, with the overarching objective of contextual language determination. To achieve this, we harness a diverse array of machine learning techniques, including Support Vector Machine, Naïve Bayes Classifier, Decision Tree, Rocchio Classification, Logistic Regression, and Random Forest. We complement these methods with two distinct feature extraction approaches: N-gram and TF-IDF. This comprehensive approach enables us to construct robust models for language identification. Our findings unveil the strong efficacy of these models in discerning Indonesian languages, with the Naïve Bayes Classifier emerging as the frontrunner, achieving an impressive accuracy rate of 99.2% with TF-IDF and an even more remarkable 99.4% with N-Gram. To gain deeper insights, we delve into error analysis, revealing that misclassifications often stem from shared words across different languages. This research is underpinned by the necessity for a robust language identification model, underscoring its critical role within the complex linguistic landscape of Indonesian regional languages. These results hold great promise for applications in automated language processing and understanding within this diverse and multifaceted linguistic context.
Pengembangan Aplikasi Pencatatan Keuangan pada Desa Wisata Karang Sidemen dalam Upaya Peningkatan Potensi Perekonomian Daerah Frima, Mariana; Firli, Anisah; Rahadian, Dadan; Rikumahu, Brady; Romadhony, Ade
Jurnal Pengabdian Masyarakat Akademisi Vol. 3 No. 3 (2024)
Publisher : Jurnal Pengabdian Masyarakat Akademisi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54099/jpma.v3i3.1034

Abstract

Desa Wisata Karang Sidemen merupakan salah satu desa di Kabupaten Lombok Tengah, Kecamatan Batukliang Utara. Desa ini memiliki berbagai destinasi wisata unggulan yaitu Danau Biru, Taman Hutan Raya Nuraksa, Glamping Lembah Surga, Lembah Gaharu, Penimproh datu bajang, Pemandian Awet Muda Nyeredet, dan River Tubing. Namun, hasil observasi menunjukkan bahwa pengelolaan yang dilakukan oleh pengelola dirasa belum maksimal dan belum siap untuk menghadapi transformasi digital sehingga membatasi cakupan pasar wisatawan yang berkunjung. Maka dari itu, studi ini dilakukan untuk membahas upaya yang telah dilakukan untuk mendukung proses transformasi digital melalui program digitalisasi keuangan, karena pengembangan desa ini dirasa potensial, terutama dengan dengan predikat sebagai desa wisata. Berdasarkan hasil dari kegiatan ini dapat disimpulkan bahwa peningkatan peran teknologi e-wallet yang telah dilakukan masih merupakan tahapan persuasi atau tahapan kedua dari model bauran teknologi (Rogers, 1983). Dalam haal proses transformasi digital, masyarakat masih akan membutuhkan bantuan lanjutan untuk mendukung tahapan 2, yaitu penguasaan teknologi (Haryanti et al., 2023). Kedepannya akan diperlukan berbagai kegiatan kunjungan serupa untuk terus menerus membantu mengedukasi pelaku usaha desa wisata sehingga dapat sampai melewati tahapan selanjutnya dalam proses bauran teknologi dan transformasi digital.
Co-Authors A, Subaveerapandiyan Aditia Rafif Khoerulloh Adiwijaya Affan Fattahila, Ananda Agung Toto Wibowo Al Aufar, Arya Prima Al Faraby, Said Alfian Akbar Gozali Ali Ridho Fauzi Rahman Ananda Wulandari Anditya Arifianto Anisa Herdiani Anisah Firli Ardiansyah, Yusfi Arya Prima Al Aufar Bambang Pudjoatmodjo Bambang Pudjotatmodjo Barawi, Mohamad Hardyman Bedy Purnama Bhudi Jati Prio Utomo Bimmo Satryo Wicaksono Brady Rikumahu Dadan Rahadian Dade Nurjanah Dana Kusumo Dana S Kusumo Dana S Kusumo Dodi Wisaksono Sudiharto Donni Richasdy Ema Rachmawati Ema Rachmawati Fat'hah Noor Prawira Fat’hah Noor Prawira Fat’hah Noor Prawira Fazainsyah Azka Wicaksono Fazmah Arif Yulianto Frima, Mariana Gheartha, I Gusti Bagus Yogiswara H Hasmawati Hamdy Nur Saidy Haryo Adi Nugroho Haryo Adi Nugroho Haryo Nugroho Hasmawat, Hasmawat Hasmawati Hasmawati Hasmawati Hasmawati Hasmawati Herman, Fizio Ramadhan Imelda Atastina Januarahman, Faishal Kemas Rahmat S.W Kemas Rahmat Saleh Wiharja Lintani Afina Hajar Raudhoti Luh Putri Ayu Ningsih Mahmud Dwi Sulistiyo Moch Arif Bijaksana Muhammad Arzaki Muhammad Aziz Pratama Muhammad Farrel Muhammad Iqbal Muhammad Iqbal Muhammad Taufik Wahdiat Muhammad Zaky Aonillah Nadine Azhalia Purbani Ningsih, Shabrina Retno Nugraha, Azhar Baihaqi Nur, Farhan Ahmadi Javier othman, mohd kamal Pramana, Rifki Adi Prawita, Fat’hah Noor Putu Harry Gunawan Ramanti Dharayani Rhesa Hermawan Ridea Valentini Peristiwari Siwabessy Rimba Whidiana Ciptasari Riska Junia Wulandari Rita Rismala Said Faraby Selly Meliana Setiawan, Muhammad Rizki Ramadhan Siti Saadah Tresna Ariesta, Bayu Untari Novia Wisesty Wijaya, Kurniadi Ahmad