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All Journal JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Creative Information Technology Journal Techno Nusa Mandiri : Journal of Computing and Information Technology JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL ILMIAH INFORMATIKA JurTI (JURNAL TEKNOLOGI INFORMASI) Martabe : Jurnal Pengabdian Kepada Masyarakat Krea-TIF: Jurnal Teknik Informatika CSRID (Computer Science Research and Its Development Journal) Informasi Interaktif CCIT (Creative Communication and Innovative Technology) Journal Indonesian Journal of Business Intelligence (IJUBI) IJIIS: International Journal of Informatics and Information Systems Jurnal Pembelajaran Pemberdayaan Masyarakat (JP2M) Management and Sustainable Development Journal Journal of Applied Data Sciences International Journal of Computer and Information System (IJCIS) Jurnal Pengabdian Kepada Masyarakat Patikala Journal Computer Science and Informatic Systems : J-Cosys Jurnal Ilmiah Edutic : Pendidikan dan Informatika Jurnal Informatika: Jurnal Pengembangan IT ABDIKOM
Musthofa Galih Pradana
Universitas Pembangunan Nasional Veteran Jakarta
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 Languange, Linguistic, Communication & Media Library & Information Science Social Sciences

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Classification of Stroke Opportunities with Neural Network and K-Nearest Neighbor Approaches Arifuddin, Nurul Afifah; Pinastawa, I Wayan Rangga; Anugraha, Nurhajar; Pradana, Musthofa Galih
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 2 (2023): Research Article, Volume 7 Issue 2 April, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.12228

Abstract

Stroke is one of the deadly diseases. This is illustrated in stroke deaths in Indonesia which reached a death rate of 131.8 cases. Some of the things that cause a stroke to become a disease with the highest mortality rate are related to transitions in human life in 4 aspects, namely epidemiology, demography, technology, and economics, socio-culture. Of the many influencing aspects, one of the transition points of human life in the technological aspect can be an alternative solution and prevention. Aspects of technology with the utilization of data can be used as a preventive measure for stroke. One approach is to use data mining techniques, which can provide an initial picture regarding the chances of getting a stroke so that it can be used as an early warning for patients. With so many techniques in data mining, this study used a classification or grouping approach using 2 algorithms, namely K-Nearest Neighbor and one of the Neural Network groups, namely Multi-Layer Perceptron. This research will focus on finding the accuracy and best results of the two algorithms in classifying. The final result of this study is that the K-Nearest Neighbor algorithm has a better accuracy of 95% compared to the Multi-Layer Perceptron which produces an accuracy of 88%
Edge Detection Model Performance Using Canny, Prewitt and Sobel in Face Detection Pinastawa, I Wayan Rangga; Pradana, Musthofa Galih; Khoironi, Khoironi
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13497

Abstract

Detection of objects in the form of objects, humans and other objects at this time has been widely applied in many aspects of life. The help of this technology can facilitate human work, one of which is facial detection to get information about a person's identity. Face identification and detection is closely related to Data Mining science with Image Processing sub-science. This facial detection and recognition can use several technical approaches, one of which is to use edge detection. Edge detection is one of the basic operations of image processing. In the image classification process, edge detection is required before image segmentation processing. There are several methods that can be used to perform edge detection such as Canny, Prewitt and Sobel. These three methods are methods that have accurate and good detection results, with the advantages of each method having its own added value. From the results of previous studies that stated these three methods have good results, it became interesting to conduct a comparative study of these three methods in detecting edges in facial images. Edge detection applied to this study identifies facial images, and will get similarities with the original image from the result analysis process, and is reinforced by measurement results using the Mean Square Error error degree. The final result of this study states that this study the most optimal Mean Square Error measurement results obtained the final results in the Canny method of 10, the Prewitt method of 41 and Sobel of 29. These results show that the value of the Canny method has the smallest Mean Square Error value, which indicates that the Canny method on facial image edge detection has the most optimal results.
Maximizing Strategy Improvement in Mall Customer Segmentation using K-means Clustering Pradana, Musthofa Galih; Ha, Hoang Thi
Journal of Applied Data Sciences Vol 2, No 1: JANUARY 2021
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v2i1.18

Abstract

The application of customer segmentation is very vital in the world of marketing, a manager in determining a marketing strategy, knowing the target customer is a must, otherwise it will potentially waste resources to pursue the wrong target. Customer segmentation aims to create a relationship with the most profitable customers by designing the most appropriate marketing strategy. Many statistical techniques have been applied to segment the market but very large data are very influential in reducing their effectiveness. The aim of clustering is to optimize the experimental similarity within the cluster and to maximize the dissimilarity in between clusters. In this study, we use K-means clustering as the basis for the segmentation that will be carried out, and of course, there are additional models that will be used to support the research results. As a result, we have succeeded in dividing the customer into 5 clusters based on the relationship between annual income and their spending score, and it has been concluded that customers who have high-income levels & have a high spending score are also very appropriate targets for implementing market strategies.
Implementasi Algoritma Cosine Similarity dan TF-IDF dalam Menentukan Rumpun Jabatan Saputra, Rangga; Jayanta; Galih Pradana, Musthofa
Krea-TIF: Jurnal Teknik Informatika Vol 12 No 1 (2024): Krea-TIF 2024
Publisher : Fakultas Teknik dan Sains, Universitas Ibn Khaldun Bogor

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

Abstract

Pada tahun 2019, sebuah instansi pemerintah memperkenalkan sistem rumpun jabatan untuk meningkatkan efisiensi dalam penugasan jabatan pegawai. Namun, tantangan muncul ketika data pegawai tahun sebelumnya tidak memiliki klasifikasi rumpun jabatan, dan data yang tersedia berupa teks dalam jumlah besar. Dikarenakan jumlah pegawai yang banyak, informasi yang melimpah, dan data yang dikelola merupakan data teks dalam jumlah yang besar. Proses pengklasifikasian manual menjadi tidak efisien. Untuk mengatasi permasalahan ini, diperlukan metode bantu yang dapat memproses data dengan cepat dan akurat. Salah satu pendekatan yang digunakan adalah Cosine Similarity menggunakan metode TF-IDF. Evaluasi hasil menunjukkan bahwa metode ini memberikan rata – rata precision sebesar 74%. Lebih rinci, nilai precision untuk keluarga jabatan dan fungsi secara berurutan mencapai 89% dan 81%. Namun, dalam mengklasifikasikan peran, tingkat precision yang dihasilkan rendah sebesar 52%.
Comparison of ARIMA and GRU Methods in Predicting Cryptocurrency Price Movements Pinastawa, I Wayan Rangga; Pradana, Musthofa Galih; Setiawan, Deandra Satriyo; Izzety, Aurel
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14235

Abstract

This study compares the effectiveness of the ARIMA and GRU models in predicting Bitcoin price movements, addressing the need for reliable predictive tools amidst the high volatility of the cryptocurrency market. Previous research has highlighted the strengths of each model in financial forecasting: ARIMA for short-term, stationary data and GRU for capturing complex temporal patterns. The purpose of this study is to evaluate which model performs better in the context of Bitcoin price prediction, offering insights for investors to minimize risks and enhance decision-making in this unpredictable market. The research methodology involves applying both models to Bitcoin price data and comparing their accuracy using the Mean Absolute Percentage Error (MAPE) across various forecasting intervals. Results indicate that GRU achieves higher accuracy in long-term forecasts, while ARIMA performs optimally for shorter time frames. However, both models demonstrate limitations, especially as the prediction horizon extends, underscoring the inherent challenges of cryptocurrency price forecasting. These findings suggest that GRU may be better suited for longer investment horizons, while ARIMA remains effective for short-term predictions. The conclusions affirm the potential of using these models selectively to align with specific investment strategies in cryptocurrency markets, although further research is recommended to improve predictive accuracy under evolving market conditions.
Deteksi Kemiripan Dokumen Menggunakan Cosine Similarity Berdasarkan Representasi Teks Count Vectorizer Dan TF IDF Pradana, Musthofa Galih; Irzavika, Nindy; Maulana, Nurhuda
Indonesian Journal of Business Intelligence (IJUBI) Vol 7, No 2 (2024): Indonesian Journal of Business Intelligence (IJUBI)
Publisher : Universitas Alma Ata

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21927/ijubi.v7i2.5170

Abstract

Tujuan mata kuliah skripsi atau tugas akhir menumbuhkan budaya berpikir kritis, dan menunjukan kemampuan untuk memecahkan permasalahan dengan konstruksi logis dari penelitian. Akan tetapi, dari banyaknya manfaat tersebut, ada beberapa permasalahan yang juga muncul dikarenakan mata kuliah ini. Plagiarisme adalah masalah umum. Mengambil karya orang lain, termasuk pendapat mereka sendiri, dan membuatnya seperti karya sendiri adalah plagiarisme. Langkah pertama dalam penggunaan teknologi adalah mendeteksi kesamaan dokumen sejak dini. Dalam hal ini, dokumen yang harus dikumpulkan oleh mahasiswa selama proses pengajuan judul skripsi mereka adalah abstrak. Ketika digunakan, algoritma cosine similarity adalah algoritma yang efisien secara komputasi karena sangat mudah dipahami dan dapat digunakan dengan data berskala besar. Penelitian ini dilakukan dengan dua pendekatan representasi teks yaitu dengan menggunakan TF-IDF dan Count Vectorizer. Data korpus yang digunakan dalam penelitian ini adalah 1600 data dokumen abstrak skripsi mahasiswa, dengan pengujian menggunakan 30 data untuk melihat kinerja algoritma cosine similarity dalam mendeteksi kesamaan dokumen abstrak. Hasil penelitian menunjukkan bahwa pendekatan representasi teks TF-IDF mendapatkan kesamaan di angka 7,72861 dan Count Vectorizer mendapatkan hasil di angka 16,85541 atau punya gap sebesar 9,1268 dengan keunggulan Count Vectorizer. Hal ini disebabkan Count Vectorizer menghitung frekuensi kata tanpa mempertimbangkan apakah kata tersebut umum atau jarang, sehingga kata-kata umum tetap berkontribusi penuh terhadap similarity.
PENERAPAN KONSEP COMPUTATIONAL THINKING MELALUI KOMPETENSI PEDAGOGIK GURU DALAM PEMBELAJARAN DI SD NEGERI 032 TILIL BANDUNG MELALUI MEDIA GAME Adrezo, Muhammad; Galih Pradana, Musthofa; Niqotaini, Zatin; Pinastawa, I Wayan Rangga; Maulana, Nurhuda; Alvionita Simanjuntak, Anni; Devira Ayu Martini, Ni Putu
Martabe : Jurnal Pengabdian Kepada Masyarakat Vol 7, No 11 (2024): MARTABE : JURNAL PENGABDIAN MASYARAKAT
Publisher : Universitas Muhammadiyah Tapanuli Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31604/jpm.v7i11.4901-4910

Abstract

Salah satu kemampuan yang penting saat ini adalah pemecahan masalah serta pemikiran logis dan sistematis dengan menguraikan masalah menjadi bagian-bagian kecil sehingga mudah untuk dijalankan sesuai dengan konsep dari Computational Thinking. Kemampuan ini yang sangat berguna di banyak aspek dalam menjalankan kehidupan, baik dalam kehidupan sehari-hari maupun secara profesional. Dalam konteks Pendidikan di Sekolah Dasar, stimulus untuk berpikir secara logis dan rasional dapat membantu untuk menyiapkan siswa yang siap untuk mempersiapkan masa depan yang menuntut kemampuan pemecahan masalah yang baik. Keterkaitan konsep Computational Thinking dengan kondisi pembelajaran di SDN 032 Tilil Bandung berdasarkan observasi dan wawancara awal dengan pihak sekolah yang menyatakan belum ada proses pembelajaran yang menstimulus konsep dan cara berpikir komputasional. Pihak sekolah menyatakan juga bahwa membutuhkan proses penyelarasan konsep Computational Thinking dalam pembelajaran, hal ini merupakan salah satu hal yang ingin diterapkan untuk mampu mewujudkan salah satu misi yang telah dicanangkan yaitu dengan melatih pola pikir anak agar mampu menumbuhkan kreativitasnya. Dalam rangka perwujudan misi SDN 032 Tilil ini dapat dilakukan dalam kegiatan pelatihan pembuatan game sederhana menggunakan game Scratch sebagai media ajar dalam proses penerapan dan integrasi konsep Computational Thinking. Kegiatan ini akan ditargetkan dalam pelatihan kepada guru yang diharapkan nantinya guru dapat memberikan pengajaran pembuatan game sederhana kepada murid dalam penerapan dan integrasi Computational Thinking.
BIMBINGAN TEKNIS METODE STEAM BERBASIS AUGMENTED REALITY UNTUK MENINGKATKAN KOMPETENSI PEDAGOGIK GURU Galih Pradana, Musthofa; Muthia Zahrah, Nada; Maulana, Nurhuda
Martabe : Jurnal Pengabdian Kepada Masyarakat Vol 7, No 12 (2024): MARTABE : JURNAL PENGABDIAN MASYARAKAT
Publisher : Universitas Muhammadiyah Tapanuli Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31604/jpm.v7i12.4964-4971

Abstract

Nilai skor PISA di Indonesia pada bidang matematika, membaca dan sains cenderung mengalami penurunan dalam tiga periode pelaksanaan tes. Tes PISA adalah tes yang mengukur kemampuan siswa dalam aspek matematika, sains dan literasi. Hal ini juga terjadi dan dirasakan oleh Guru di Sekolah Dasar Negeri Tanjungsari 03 dengan masalah yang terjadi pada aspek matematika, sains dan literasi yang perlu ditingkatkan. Salah satu hal yang bisa dilakukan adalah bagaimana membuat pembelajaran pada aspek matematika, sains dan literasi dibuat menjadi lebih menyenangkan dan tidak membosankan. Hal ini bisa dilakukan dengan mengadopsi metode STEAM ke dalam pembelajaran, selain itu media ajarnya juga dapat diberikan variasi dengan menggunakan teknologi, sehingga siswa menjadi lebih antusias dalam mengikuti kegiatan belajar mengajar. Pembelajaran berbasis teknologi dapat menjadi solusi dengan menerapkan Augmented Reality, dengan pembelajaran berbasis Augmented Reality siswa akan bisa mengenali konteks pembelajaran menjadi lebih mudah, karena divisualisasikan ke dalam objek 3D yang menarik, dan tanpa mengesampingkan informasi penting yang didapatkan melalui menu informasi di aplikasi. Kegiatan ini dilakukan dengan bimbingan teknis kepada Guru di SD Negeri Tanjungsari 03 dengan penguatan aspek pedadogik, pengenalan dan pemahaman metode STEAM, serta demonstrasi aplikasi pembelajaran berbasis Augmented Reality. Hasil kuisioner menunjukkan rata-rata responden menyatakan setuju dengan nilai kebermanfaatan kegiatan ini dengan nilai rerata sebesar 4,06, dan memiliki nilai peningkatan dari skor pre test ke post test sebesar 11,5.
UNVEILING GENDER FROM INDONESIAN NAMES USING RANDOM FOREST AND LOGISTIC REGRESSION ALGORITHMS Pradana, Musthofa Galih; Saputro, Pujo Hari; Tyas, Dyah Listianing
Jurnal Techno Nusa Mandiri Vol. 21 No. 2 (2024): Techno Nusa Mandiri : Journal of Computing and Information Technology Period o
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/techno.v21i2.5537

Abstract

Gender detection can be done in many ways, some of these ways by using image identification such as the process of image identification based on faces or image shapes, on the other hand image identification and detection can also be done based on text or written data. The usefulness of gender identification can be used in various aspects of life, ranging from greetings such as ladies and gentlemen, which will certainly make the person concerned feel more appreciated by the accuracy of the pronunciation of the name. This gender identification and detection process can be done by making class predictions on predetermined gender label classes. Of course, each name in various languages has different characteristics in identifying and representing each gender, as well as Indonesian names that have diversity and unique levels of variation. The purpose of this study is to test the results of the algorithm in classification based on class labels. The application of this detection uses two algorithms, namely Random Forest and Logistic Regression. Both of these algorithms can predict classes with perfect accuracy in 6 experimental data, then the results of 526 experimental data resulted in a final accuracy of 0.94 for logistic regression and 0.93 for random forest. The advantage with a thin difference in this case is in the Logistic Regression algorithm.
Performance Improvement of Cosine Similarity Algorithm with Bidirectional Encoder Representations from Transformers on Abstract Document Similarity Detection Pradana, Musthofa Galih; Irzavika, Nindy; Maulana, Nurhuda; Mu, Jesselyn; Wari, Valtrizt Khalifah
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.2853

Abstract

In thesis courses or final projects, students are required to be able to conduct research by the science they are engaged in, find innovations, solve problems, and foster a culture and critical mindset. However, the issue that is often encountered is plagiarism. Plagiarism is taking a work that can be in the form of someone else's opinion and making it seem as if it is your own. The step in applying technology that can be done is to carry out early detection of the similarity of documents written by students. In this case, the document that will be detected is an abstract that must be collected by students when submitting a thesis title. The algorithm used is a cosine similarity algorithm, which is computationally efficient because of its ease of interpretation and compatibility with large-scale data. This research was carried out using two schematic approaches: bidirectional encoder representations from transformers (BERT) and not bidirectional encoder representations from transformers (BERT). The corpus data used in this study was 1450 data of student thesis abstract documents, with the test using 10 data to see the performance of the cosine similarity algorithm in detecting the similarity of abstract documents. The results showed that documents with optimization using the Bidirectional Encoder Representations from Transformers (BERT) approach had better results, with an average performance improvement of 23.48%.
Co-Authors Adhien Kenya Anima Estetikha Andri Pramuntadi Anni Alvionita Simanjuntak Budi, Ryan Setya Dema Mathias Lumban Tobing Dhina Puspasari Wijaya, Dhina Puspasari Dwi Nyamiati, Retno Dyah Listianing Tyas, Dyah Listianing Emha Taufiq Luthfi Fadhli Suko Wiryanto Fandli Supandi Gutama, Deden Hardan Ha, Hoang Thi Henki Bayu Seta I Wayan Rangga Pinastawa Izzety, Aurel Jayanta Kaharuddin Kharisma Wiati Gusti Khoironi, Khoironi Khoirunnisa, Hilda Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini MARTINI, NI PUTU DEVIRA AYU Mu, Jesselyn Muhamad Ridwan Muhamad Ridwan Muhammad Adrezo Muthia Zahrah, Nada Neny Rosmawarni Nindy Irzavika Niqotaini, Zatin Nurcahyo, Azriel Christian Nurhajar Anugraha Nurhuda Maulana Nurul Afifah Arifuddin Nyamiati, Retno Dwi Pamekas, Bondan Wahyu Prananto, Muhammad Teguh Pujo Hari Saputro Radinal Setyadinsa Rangga Pinastawa, I Wayan Rangga Saputra Ridlo Muttaqien Ridwan Raafi'udin Rifka Dwi Amalia Rifqi Hammad Rusiyono, Ruwet Setiawan, Deandra Satriyo Wahit Desta Prastowo Wari, Valtrizt Khalifah Yulianto Mustaqim Yusuf Hendra Pratama