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Iptek bagi Masyarakat untuk Melestarikan Kebudayaan Ketoprak dan Sholawat Pitutur Melalui Website Berbasis SEO Sebagai Media Informasi dan Promosi Hayaty, Mardhiya; Laksito, Arif Dwi
JPP IPTEK (Jurnal Pengabdian dan Penerapan IPTEK) Vol 1, No 1 (2017)
Publisher : LPPM ITATS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (604.647 KB) | DOI: 10.31284/j.jpp-iptek.2017.v1i1.136

Abstract

Ketoprak and sholawat pitutur began to dim with the development of the modern era. Folk art that became the cultural root of the village community Seyegan must continue to be preserved. To preserve the efforts made through the promotion through information technology media in this case the manufacture of SEO-based art sites (Search Engine Optimazation) for the existence of this art continues to grow with the local community and can be known by the public. The process of website creation begins with several designs such as web structure, layout, features available as well as the application of SEO on some articles. Promotion of folk art through the medium of information technology is expected to revive the arts ketoprak and sholawat as well as affect the welfare of members of the arts group.
Random and Synthetic Over-Sampling Approach to Resolve Data Imbalance in Classification Mardhiya Hayaty; Siti Muthmainah; Syed Muhammad Ghufran
International Journal of Artificial Intelligence Research Vol 4, No 2 (2020): December 2020
Publisher : STMIK Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v4i2.152

Abstract

High accuracy value is one of the parameters of the success of classification in predicting classes. The higher the value, the more correct the class prediction.  One way to improve accuracy is dataset has a balanced class composition. It is complicated to ensure the dataset has a stable class, especially in rare cases. This study used a blood donor dataset; the classification process predicts donors are feasible and not feasible; in this case, the reward ratio is quite high. This work aims to increase the number of minority class data randomly and synthetically so that the amount of data in both classes is balanced. The application of SOS and ROS succeeded in increasing the accuracy of inappropriate class recognition from 12% to 100% in the KNN algorithm. In contrast, the naïve Bayes algorithm did not experience an increase before and after the balancing process, which was 89%. 
Perancangan Sistem Penunjang Keputusan Menggunakan Kombinasi Algoritma Simple Multi Attribute Rating Technique (SMART) dan Forward Chaining Hayaty, Mardhiya; Irawan, Restu Fajri
Khazanah Informatika Vol. 4 No. 2 Desember 2018
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v4i2.7034

Abstract

Pada era sekarang kebutuhan akan informasi sangat tinggi, sehingga diperlukan pemprosesan data yang cepat, akurat, dan tepat. Informasi yang dihasilkan harus berkualitas sehingga dapat digunakan ?untuk pengambilan keputusan. Algoritma Simple Multi Attribute Rating Technique (SMART) adalah algoritma yang digunakan untuk pengambilan keputusan dengan menggunakan multi kriteria, ?setiap kriteria memiliki nilai dan diberikan kepada setiap alternatif sehingga menghasilkan jumlah peringkat pembobotan untuk mendapatkan alternatif terbaik. Penggunaan algoritma SMART tidak dapat menentukan hasil secara spesifik, tetapi hanya berdasarkan peringkat tertinggi, untuk mengatasi hal tersebut penelitian dikombinasikan dengan algoritma Forward Chaining. Proses algoritma Forward Chaining mensinkronkan fakta-fakta yang ada dengan rule yang telah ditetapkan dengan cara penelusuran secara runtut maju melalui penalaran logika IF-THEN. Kombinasi kedua algoritma tersebut telah menghasilkan sebuah sistem pendukung keputusan yang digunakan tidak hanya untuk penyeleksian calon anggota sebuah organisasi tetapi sistem dapat memberikan rekomendasi posisi atau jabatan kepada anggota tersebut.
The Effect of Stemming and Removal of Stopwords on the Accuracy of Sentiment Analysis on Indonesian-language Texts Aditya Wiha Pradana; Mardhiya Hayaty
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol 4, No 4, November 2019
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (317.101 KB) | DOI: 10.22219/kinetik.v4i4.912

Abstract

Preprocessing is an essential task for sentiment analysis since textual information carries a lot of noisy and unstructured data. Both stemming and stopword removal are pretty popular preprocessing techniques for text classification. However, the prior research gives different results concerning the influence of both methods toward accuracy on sentiment classification. Therefore, this paper conducts further investigations about the effect of stemming and stopword removal on Indonesian language sentiment analysis. Furthermore, we propose four preprocessing conditions which are with using both stemming and stopword removal, without using stemming, without using stopword removal, and without using both. Support Vector Machine was used for the classification algorithm and TF-IDF as a weighting scheme. The result was evaluated using confusion matrix and k-fold cross-validation methods. The experiments result show that all accuracy did not improve and tends to decrease when performing stemming or stopword removal scenarios. This work concludes that the application of stemming and stopword removal technique does not significantly affect the accuracy of sentiment analysis in Indonesian text documents.
Implementasi Software Plagiasi dan Google Classroom Untuk Membantu Penilaian Tugas Siswa Pada SMK Nasional Berbah-Seleman Mardhiya Hayaty
Jurnal ABDINUS : Jurnal Pengabdian Nusantara Vol 3 No 2 (2020): Volume 3 Nomor 2 Tahun 2020
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/ja.v3i2.13812

Abstract

Work on student assignments and assessments are done manually so that evaluation cannot be done objectively because many tasks are similar or even the same as other student assignments. Copying other people's work is unlawful; students lack an understanding of the definition of plagiarism. Therefore education about this is done early, especially for the world of knowledge, which incidentally really appreciates the work of others. Making plagiarism software is needed to answer these challenges; this service activity provides training to teachers in managing online-based student assignments and checking assignment documents using plagiarism software. This activity can make it easier for teachers to offer assignment assessments and provide students with an understanding of the originality of a work.
Perbandingan Akurasi dan Waktu Proses Algoritma K-NN dan SVM dalam Analisis Sentimen Twitter Muhammad Rangga Aziz Nasution; Mardhiya Hayaty
Jurnal Informatika Vol 6, No 2 (2019): September 2019
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (940.904 KB) | DOI: 10.31294/ji.v6i2.5129

Abstract

Salah satu cabang ilmu komputer yaitu pembelajaran mesin (machine learning) menjadi tren dalam beberapa waktu terakhir. Pembelajaran mesin bekerja dengan memanfaatkan data dan algoritma untuk membuat model dengan pola dari kumpulan data tersebut. Selain itu, pembelajaran mesin juga mempelajari bagaimama model yang telah dibuat dapat memprediksi keluaran (output) berdasarkan pola yang ada. Terdapat dua jenis metode pembelajaran mesin yang dapat digunakan untuk analisis sentimen:  supervised learning dan unsupervised learning. Penelitian ini akan membandingkan dua algoritma klasifikasi yang termasuk dari supervised learning: algoritma K-Nearest Neighbor dan Support Vector Machine, dengan cara membuat model dari masing-masing algoritma dengan objek teks sentimen. Perbandingan dilakukan untuk mengetahui algoritma mana lebih baik dalam segi akurasi dan waktu proses. Hasil pada perhitungan akurasi menunjukkan bahwa metode Support Vector Machine lebih unggul dengan nilai 89,70% tanpa K-Fold Cross Validation dan 88,76% dengan K-Fold Cross Validation. Sedangkan pada perhitungan waktu proses metode K-Nearest Neighbor lebih unggul dengan waktu proses 0.0160s tanpa K-Fold Cross Validation dan 0.1505s dengan K-Fold Cross Validation.
Perancangan Sistem Penunjang Keputusan Menggunakan Kombinasi Algoritma Simple Multi Attribute Rating Technique (SMART) dan Forward Chaining Mardhiya Hayaty; Restu Fajri Irawan
Khazanah Informatika Vol. 4 No. 2 Desember 2018
Publisher : Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v4i2.7034

Abstract

Pada era sekarang kebutuhan akan informasi sangat tinggi, sehingga diperlukan pemprosesan data yang cepat, akurat, dan tepat. Informasi yang dihasilkan harus berkualitas sehingga dapat digunakan  untuk pengambilan keputusan. Algoritma Simple Multi Attribute Rating Technique (SMART) adalah algoritma yang digunakan untuk pengambilan keputusan dengan menggunakan multi kriteria,  setiap kriteria memiliki nilai dan diberikan kepada setiap alternatif sehingga menghasilkan jumlah peringkat pembobotan untuk mendapatkan alternatif terbaik. Penggunaan algoritma SMART tidak dapat menentukan hasil secara spesifik seperti rekomendasi  posisi jabatan sebuah organisasi, tetapi hanya berdasarkan peringkat tertinggi sebagai dasar seleksi calon pengurus, untuk mengatasi hal tersebut penelitian dikombinasikan dengan algoritma Forward Chaining. Proses algoritma Forward Chaining mensinkronkan fakta-fakta yang ada dengan rule yang telah ditetapkan dengan cara penelusuran secara runtut maju melalui penalaran logika IF-THEN, sehingga implementasi algortima Forward Chaining  dapat merekomendasian posisi jabatan calon pengurus pada sebuah organisasi. Penelitian ini telah menghasilkan 25 orang kandidat pengurus organisasi dengan nilai terbaik dan sekaligus merekomendasikan posisi jabatan  sekretaris sebanyak 3 orang, 2 orang bendahara, 4 orang jabatan aspirasi, bagian humas 4 orang, LITBANG berjumlah 5 orang, keorganisasian sebanyak 5 orang, 1 orang jabatan LITBANG atau jabatan aspirasi serta jabatan bendahara atau sekretaris sebanyak 1 orang
Lexicon-Based Indonesian Local Language Abusive Words Dictionary to Detect Hate Speech in Social Media Mardhiya Hayaty; Sumarni Adi; Anggit Dwi Hartanto
Journal of Information Systems Engineering and Business Intelligence Vol. 6 No. 1 (2020): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.6.1.9-17

Abstract

Background: Hate speech is an expression to someone or a group of people that contain feelings of hate and/or anger at people or groups. On social media users are free to express themselves by writing harsh words and share them with a group of people so that it triggers separations and conflicts between groups. Currently, research has been conducted by several experts to detect hate speech in social media namely machine learning-based and lexicon-based, but the machine learning approach has a weakness namely the manual labelling process by an annotator in separating positive, negative or neutral opinions takes time long and tiringObjective: This study aims to produce a dictionary containing abusive words from local languages in Indonesia. Lexicon-base is very dependent on the language contained in dictionary words. Indonesia has thousands of tribes with 2500 local languages, and 80% of the population of Indonesia use local languages in communication, with the result that a significant challenge to detect hate speech of social media.Methods: Abusive words surveys are conducted by using proportionate stratified random sampling techniques in 4 major tribes on the island of Java, namely Betawi, Sundanese, Javanese, MadureseResults: The experimental results produce 250 abusive words dictionary from 4 major Indonesian tribes to detect hate speech in Indonesian social media by using the lexicon-based approach. Conclusion: A stratified random sampling technique has been conducted in 4 major Indonesian tribes to produce 250 abusive words for hate speech detection using the lexicon-based approach.
POLA PEMBELIAN KONSUMEN DAN MENYUSUN STRATEGI PENJUALAN MENGGUNAKAN ALGORITMA APRIORI BERBASIS WEBSITE (STUDI KASUS : PT. XYZ) Mardhiya Hayaty; Wisnu Dwi Harianto
Jurnal Mantik Penusa Vol. 3 No. 1.1 (19): Manajemen dan Ilmu Komputer
Publisher : Lembaga Penelitian dan Pengabdian (LPPM) STMIK Pelita Nusantara Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (440.402 KB)

Abstract

PT. XYZ is a company engaged in the business of selling agrocomplex products including agricultural products, plantations, fisheries, health, and household products. Transaction data continues to increase, companies have difficulty knowing the patterns of consumer purchases accurately. Huge accumulation of data can be used by companies to determine strategies that can support the company's business processes. In this study, the implementation of association rule mining was carried out to help determine consumer purchasing patterns. The technique of combining mining rules used is a priori algorithm that is applied to web-based applications to analyze nasaofficial.com transaction data. To get accurate results added calculation of lift ratio. In this study determined the minimum value of support is 10% and a minimum confidence value of 60%. The result is that there are 3 items of goods, namely viterna, hormonal, nasa. The final results of this study indicate that the mining association rules using a priori algorithms have been successfully applied in applications. The highest association rule produced in the transaction data for the past year is if consumers buy viterna (natural animal vitamins) and hormonic then buy NASA POC with a support value of 23% and 96% confidence value.
Pelatihan Pembuatan Konten Pembelajaran Menggunakan Open Broadcast Software Mardhiya Hayaty; Sri Ngudi Wahyuni; Istiningsih; Andriyan Dwi Putra; Mei Maemunah; Barka Satya; Dwi Nurani
Abdiformatika: Jurnal Pengabdian Masyarakat Informatika Vol. 1 No. 2 (2021): November 2021 - Abdiformatika: Jurnal Pengabdian Masyarakat Informatika
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2580.217 KB) | DOI: 10.25008/abdiformatika.v1i2.142

Abstract

Pandemi COVID-19 terjadi terjadi diseluruh dunia, seluruh aktifitas dan kegiatan menjadi terganggu termasuk aktifitas pendidikan. Seluruh kegiatan pendidikan yang semula dilaksanakan secara offline, harus dilaksanakan secara online. Salah satu cara untuk mempermudah kegiatan belajar mengajar, adalah dengan mengunggah video yang berisi materi-materi pembelajaran ke media sosial seperti Youtube ataupun sejenisnya. Hal ini tentunya membawa perubahan yang sangat signifikan terhadap kegiatan belajar mengajar termasuk kegiatan di SD Muhammadiyah Rabbani Kabupaten Klaten Jawa Tengah. Open Broadcast Software atau OBS merupakan salah satu tools open source yang bisa dimanfaatkan untuk membuat video pembelajaran dengan mudah dan tidak terkoneksi internet. Tujuan dari pelatihan ini adalah tentang tatacara pembuatan OBS. Evaluasi menggunakan kuesioner online dan diolah menggunakan SPSS versi 25. Hasil evaluasi menunjukkan bahwa 60% peserta menjawab sangat setuju bahwa pelatihan mudah dimengerti, pemateri sangat handal dan OBS mudah di implementasikan, sedangkan 30% lainnya menjawab setuju.