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All Journal Jurnal Buana Informatika JIKO (Jurnal Informatika dan Komputer) JITK (Jurnal Ilmu Pengetahuan dan Komputer) JUTIM (Jurnal Teknik Informatika Musirawas) Jiko (Jurnal Informatika dan komputer) Jurnal Ilmiah Media Sisfo JURIKOM (Jurnal Riset Komputer) EDUMATIC: Jurnal Pendidikan Informatika Jurnal Teknologi Informasi dan Pendidikan Building of Informatics, Technology and Science JATI (Jurnal Mahasiswa Teknik Informatika) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) MANDAR: Management Development and Applied Research Journal Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Pengabdian kepada Masyarakat Nusantara Information System Journal (INFOS) INFOSYS (INFORMATION SYSTEM) JOURNAL Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Ilmiah Pengabdian dan Inovasi Jurnal Informatika: Jurnal Pengembangan IT The Indonesian Journal of Computer Science
Nuri Cahyono
Universitas Amikom Yogyakarta
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Social Sciences Other

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Analisis UI/UX dan Front End Aplikasi Desain Rumah Menggunakan Human Centered Design Salamun Sukriandi; Nuri Cahyono
Jurnal Ilmiah Media Sisfo Vol 17 No 1 (2023): Jurnal Ilmiah Media Sisfo
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/mediasisfo.2023.17.1.779

Abstract

Jasa desain rumah mulai banyak diperlukan bagi masyarakat yang ingin membangun rumah dengan desain yang. Bulihuma memiliki kesulitan untuk bersaing dengan jasa desain rumah lainnya. Platform digital yang dinilai kurang memadai dan kantor yang belum diketahui banyak orang menjadi masalah utama Bulihuma dalam mendapatkan klien. Untuk itu diperlukan sebuah aplikasi khusus yang dapat  meliputi jasa desain rumah ini secara detail. Dalam merancang sebuah aplikasi, terlebih dahulu harus dilakukan perancangan UI/UX agar nantinya aplikasi dapat dengan mudah digunakan oleh calon pengguna. Dalam perancangan aplikasi “Bulihuma” ini, menggunakan metode Human Centered Design dimana metode ini terdiri dari 3 tahapan yaitu Inspiration, Ideation, dan Implementation. Pengujian dilakukan kepada 15 pengguna yang telah memenuhi kriteria. Hasil akhir yang didapat pada penelitian ini yaitu mendapatkan 98% dalam aspek efektifitas, 91,52% dalam aspek efisiensi, dan 86,33 dalam System Usability Scale. Hasil ini termasuk dalam kategori “Acceptable” pada Acceptability Ranges, mendapatkan grade ”B” pada Grade Scale, dan masuk dalam kategori “Excellent” dalam Adjective Ratings.
HYPERPARAMETER TUNING ON RANDOM FOREST FOR DIAGNOSE COVID-19 Anna Baita; Inggar Adi Prasetyo; Nuri Cahyono
JIKO (Jurnal Informatika dan Komputer) Vol 6, No 2 (2023): JIKO (Jurnal Informatika dan Komputer)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v6i2.6389

Abstract

Diagnosis of Covid using the RT-PCR (Reverse Transcription Polymerase Chain Reaction) test requires high costs and takes a long time. For this reason, another method is needed that can be used to diagnose Covid-19 quickly and accurately. Random Forest is one of the popular classification algorithms for making predictive models. Random forest involves many hyperparameters that control the structure of each tree, the forest, and its randomness. Random Forest is a method which very sensitive to hyperparameter values, as their prediction accuracy can increase significantly when optimized hyperparameters are predefined and then adjusted according to the procedure. The purpose of doing hyperparameter tuning on the random forest algorithm is to increase accuracy in the diagnosis of covid-19. Searching for optimal values of hyperparameters is done by the Grid Search method and Random Search. The result explains that the Random Forest can be used to diagnose Covid-19 with an accuracy of 94%, and with hyperparameter tuning, it can increase the accuracy of the random forest by 2%.
Analisis Sentimen Masyarakat Terhadap Penggunaan E-Commerce Menggunakan Algoritma K-Nearest Neighbor Ikhsan Habib Kusuma; Nuri Cahyono
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 3 (2023)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i3.5734

Abstract

Abstract − E-commerce's rapid growth has resulted in an increase in online transactions and shifts in consumer behavior. In Indonesia, the use of e-commerce has grown rapidly, with many online platforms emerging. Understanding public sentiment towards e-commerce in Indonesia is crucial for businesses to improve their services and maintain customer satisfaction. In this review, study propose a methodology for feeling investigation of popular assessment on the utilization of web-based business in Indonesia, utilizing directed learning calculations. The study involved collecting data from the website Google Play Store. The study performed data preprocessing, including removing stop words, tokenization, and stemming, before applying the K-Nearest Neighbor (K-NN) algorithm to classify sentiments into positive or negative. The evaluation was conducted using confusion matrix and classification report. The results showed that the proposed approach was effective in analyzing public sentiment towards e-commerce in Indonesia, with an accuracy rate of 82%. The study concluded that the proposed strategy could help businesses enhance their services and better satisfy customers' requirements and expectations.Keywords – Sentiment Analysis, E-Commerce, Supervised Learning, Machine Learning, NLP, KNN. Abstrak - Perkembangan e-commerce yang pesat telah menyebabkan peningkatan transaksi online dan perubahan perilaku konsumen. Di Indonesia, penggunaan e-commerce tumbuh pesat dengan banyak platform online bermunculan. Memahami sentimen masyarakat terhadap e-commerce di Indonesia sangat penting bagi bisnis untuk meningkatkan layanan dan menjaga kepuasan pelanggan. Oleh karena itu, dalam penelitian ini peneliti mengusulkan sebuah pendekatan untuk melakukan analisis sentimen opini publik mengenai penggunaan salah satu e-commerce di Indonesia dengan menggunakan algoritma K-Nearest Neighbor. Pengumpulan data dilakukan dari website Google Play Store dengan tujuan untuk memperoleh pandangan dan pengalaman masyarakat terkait penggunaan salah satu e-commerce di Indonesia. Setelah data terkumpul, dilakukan proses preprocessing untuk membersihkan data, termasuk menghilangkan stopwords, tokenisasi, dan stemming. Setelah itu, algoritma K-Nearest Neighbor (K-NN) digunakan untuk mengklasifikasikan sentimen menjadi positif atau negatif. Evaluasi dilakukan dengan menggunakan confusion matrix dan classification report untuk menilai keakuratan algoritma. Hasil penelitian menunjukan bahwa pendekatan yang diusulkan efektif dalam menganalisis sentimen masyarakat terhadap e-commerce di Indonesia, dengan tingkat akurasi 82%. Penelitian ini memiliki implikasi penting bagi bisnis e-commerce di Indonesia dalam meningkatkan layanan dan memenuhi kebutuhan serta harapan pelanggan secara lebih baik.Kata Kunci - Sentimen Analisis, E-Commerce, Supervised Learning, Machine Learning, NLP, KNN.
COMPARISON OF DEEP LEARNING METHODS ON SENTIMENT ANALYSIS USING WORD EMBEDDING Rizal Gibran Aldrin Pratama; nuri cahyono
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 1 (2024): JITK Issue August 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i1.5280

Abstract

According to ICW, corruption cases in Indonesia in the last 5 years have increased and the amount of losses suffered by the state from 2012-2022 reached Rp138.39 trillion. According to Transparency International, Indonesia's CPI ranking decreased in 2023 to 115 compared to 2022 at 110 out of 180 countries. These results show that the response to corruption is still slow and continues to deteriorate due to a lack of support from stakeholders. The purpose of this study is to test and compare the performance of deep learning model algorithms (RNN/LSTM/GRU/Bi-GRU/Bi-LSTM) on sentiment classification using word embedding, and obtain a model architecture that can determine the polarity of a sentence about public sentiment related to corruption in Indonesia, which can help governments, researchers, and practitioners in designing more effective anti-corruption strategies. The dataset used amounted to 1793 derived from crawling Twitter with 3 classes namely positive, negative and neutral. This research starts from data collection, preprocessing, word embedding, splitting the dataset which is divided into 80% training data and 20% test data, deep learning model testing, model evaluation and result representation. Word embedding uses word2vec with a dimension of 300. Based on the experimental results obtained, Bi-GRU has better performance than other architectural models with an accuracy value of 88%, precision 88.07%, recall 86.97% and f1-score 87.51%. The data used in this research is relatively small, it is recommended that future research can overcome it
Implementation of Augmented Reality 3D Catalog And 2D Motion Based on Multimarker Santoso, Riki Adi; Cahyono, Nuri
Jurnal Teknologi Informasi dan Pendidikan Vol 17 No 1 (2024): Jurnal Teknologi Informasi dan Pendidikan
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtip.v17i1.816

Abstract

In the current era of technological progress, various kind of technology can be applied in multiple fields. One technology that is currently popular is Augmented Reality (AR). This technology can be used in several ways, for example, AR for the learning proccess, AR for simulations and AR for promoting a product. This research used Multimarker-based Augmented Reality technology to promote products at Essential Bakery. Essential Bakery is an MSME that operates in the food sector, one of which is bread. The research aims to help MSME Essential Bakery promote its product to make the services provided more attractive and innovative. This research uses the MDLC (Multimedia Development Life Cycle) research method. This Augmented Reality Uses Multimarker-based tracking, which is a differentiator in previsious research. Hopefully, this research will improve effective and innovative services by usong Augmented Reality as a promotional medium. It is hoped that human resources in indonesia will be more aware of the latest technology currently available. The result of this research is that the application can run 3D objects and 2D motion through the markers that have been created.
Multimarker Augmented Reality in Human Digestive System Application Using the MDLC Method Nugroho, Bagas Julio; Cahyono, Nuri
Jurnal Teknologi Informasi dan Pendidikan Vol 17 No 1 (2024): Jurnal Teknologi Informasi dan Pendidikan
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtip.v17i1.819

Abstract

Learning media has a crucial role in supporting the learning process, especially in subjects related to natural sciences. Using conventional media such as blackboards, pictures in books, and less varied teaching methods can make students feel bored, less active, and lose concentration. Therefore, this research aims to implement augmented reality technology in science subjects to create exciting and memorable learning experiences for students and encourage student involvement in the learning process. This research uses the Multimedia Development Life Cycle (MDLC) method, which applies the latest technology, Multimarker, to display more than one three-dimensional (3D) object. The research results show that learning media using Augmented Reality Technology on the human digestive system obtained a “Good” predicate and can be used in the learning process at Tuguran State Elementary School.
Analisis Sentimen Review ChatGPT di Play Store menggunakan Support Vector Machine dan K-Nearest Neighbor Pamungkas, Adji Surya; Cahyono, Nuri
Jurnal Pendidikan Informatika (EDUMATIC) Vol 8 No 1 (2024): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v8i1.24114

Abstract

The ChatGPT application for Android was launched on July 25, 2023, and the language model from OpenAI achieved a rating of 4.8 until early 2024. Despite the majority of positive reviews, user reports stating that ChatGPT provides inaccurate answers raise concerns about the reliability of this application. This research aims to compare the models of the Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) algorithms in analyzing the sentiment of ChatGPT application reviews. Utilizing text mining methods to extract information from text, data was collected from Google Play Store reviews using data scraping techniques and analyzed with Support Vector Machine and K-Nearest Neighbor algorithms. Cross-validation with 5 folds and data split using 80% training and 20% testing data were applied to evaluate the performance of both algorithms. The sentiment classification results showed that the Support Vector Machine algorithm achieved an average accuracy of 80%, while K-Nearest Neighbor reached 71%. SVM excels due to its ability to overcome KNN's limitations regarding less relevant features that do not significantly contribute to predictions. The findings of this study are expected to help developers understand and respond to user feedback regarding the reliability of ChatGPT.
PENGUJIAN ANIMASI MOTION GRAPHIC SAVE THE PLANET DENGAN METODE ALPHA DAN BETA TESTING Cahyono, Nuri; Bagus Candrahutomo, Rio
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 7 No. 1 (2023): JATI Vol. 7 No. 1
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v7i1.6147

Abstract

Animasi save the planet dibuat dengan Teknik motion graphic dan dalam pengembangan telah melalui tiga tahap pengembangan. Untuk mengetahui kelayakan dari animasi yang dibuat maka dilakukan pengujian dengan dua metode yaitu alpha testing dan beta testing. Pada alpha testing didapatkan hasil animasi tersebut memenuhi pengujian 12 prinsip animasi, memenuhi pengujian kebutuhan fungsional dan memenuhi pengujian kesesuaian terhadap storyboard. Dalam beta testing terdapat dua validasi yaitu uji aspek kelayakan cerita dengan presentase 95% dan uji aspek kelayakan animasi di peroleh presentase 92% artinya animasi sudah layak untuk dijadikan media edukasi.
ANALISIS SENTIMEN KOMENTAR MASYARAKAT TERHADAP PELAYANAN PUBLIK PEMERINTAH DKI JAKARTA DENGAN ALGORITMA SUPER VECTOR MACHINE DAN NAIVE BAYES Rakarahayu Putri, Raniya; Cahyono, Nuri
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 8 No. 2 (2024): JATI Vol. 8 No. 2
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v8i2.9472

Abstract

Sebagai pelayan masyarakat, pemerintah memiliki peran yang penting dan tanggung jawab untuk menyediakan layanan yang memadai. Dengan meningkatnya jumlah pengguna Instagram di Indonesia meberikan kemudahan bagi masyarakat dalam berkomunikasi. Ekspresi masyarakat di Instagram terhadap pelayanan Pemerintah DKI Jakarta menunjukkan kompleksitas. Komentar-komentar mencerminkan ketidakpuasan dan kritik tajam terhadap layanan public. Penelitian ini mencoba melakukan klasifikasi komentar menjadi dua kelas positive dan negative dengan menerapkan dua metode klasifikasi, yaitu Support Vector Machine (SVM) dan Multinomial Naive Bayes (MNB), dalam analisis sentimen terhadap komentar masyarakat pada akun media sosial DKI Jakarta. Tahap pemodelan mencakup pembagian data latih dan uji dengan variasi rasio, dan metode SVM dievaluasi menggunakan kernel linear dan radial basis function (RBF) dengan grid search cross-validation. Hasil menunjukkan bahwa SVM memberikan akurasi yang sedikit lebih tinggi daripada MNB, mencapai 82%. Parameter optimal untuk SVM adalah C=100 dan gamma=0.1 pada kernel RBF. Pada MNB, parameter alpha=2.0 dan fit_prior=True memberikan kinerja optimal dengan akurasi 80%. Evaluasi dilakukan dengan confusion matrix dan 10-folds cross validation. Meskipun SVM sedikit lebih unggul, penelitian selanjutnya direkomendasikan untuk eksplorasi teknik-teknik baru dalam machine learning dan pengembangan model klasifikasi yang lebih kompleks. Penelitian ini memberikan kontribusi penting dalam memahami sentimen masyarakat terhadap pelayanan publik DKI Jakarta, membuka pintu untuk pengembangan analisis sentimen lebih lanjut dengan metode-metode machine learning
ANALISIS SENTIMEN KOMENTAR INSTAGRAM PADA PROGRAM KAMPUS MERDEKA DENGAN ALGORITMA NAIVE BAYES DAN DECISION TREE Wicaksono, Bayu; Cahyono, Nuri
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 8 No. 2 (2024): JATI Vol. 8 No. 2
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v8i2.9473

Abstract

Instagram merupakan salah satu sosial media yang digunakan merepresentasikan diri, berinteraksi, dan mencari informasi. Kita dapat mengambil sekumpulan informasi ke dalam bentuk dataset untuk diolah lebih lanjut. Berkaitan dengan hal itu , Program Kampus Merdeka sebagai objek analisis, mengingat Program Kampus Merdeka adalah program pemerintah yang saat ini sedang dijalanan oleh Kemendikbud. Pengambilan dataset yang didapat dari kumpulan komentar Instragram, tool yang digunakan adalah phantombuster. Menggunakan bahasa pemrograman phyton dengan tools Google Collab, dengan Algoritma Naïve Bayes Classifier dan Decision Tree untuk membuat model sentimen. Hasil scrapping mendapatkan 1764 data , dan sesudah dilakukan pre-processing menjadi 1694 data. Dari sentimen analisis yang telah dilakukan diperoleh hasil dari penerapan Algortima Complement Naïve Bayes dan Decision Tree, sebelum dilakukan SMOTE over-sampling, perbandingan data positif dan negatif sebesar 35,06% banding 64,95% , dengan akurasi model Decision Tree 84% dengan skenario pembagian data 90:10 dan model Complement Naïve Bayes 81% pada skenario pembagian data 80:20. Setelah dilakukan balancing data menggunakan SMOTE over-sampling, akurasi pada model Decision Tree naik sebesar 1% dari 86% menjadi 85%, dengan skenario pembagian data 90:10, dan pada model Complement Naïve Bayes juga mengalami kenaikan sebesar 2%, dari 82% menjadi 83% dengan skenario pembagian data 80:20
Co-Authors Adi Saputro, Johan Afnan Wijaya, Hafid Alfataah, Sevilla Andika Joni, Gusti Angga Reni Dwi Astuti Angga Reni Dwi Astuti Anggista Oktavia Praneswara Anggita, Sharazita Dyah Anggoro Saputro, Uyock Anna Baita Arif Rahmat Hidayatullah Arifiyanto Hadi Negoro Arifiyanto Hadinegoro, Arifiyanto Armadyah Amborowati Armadyah Amborowati Armadyah Amborowati Bagus Candrahutomo, Rio Baita, Anna Bayu Prayoga, Mahendra Bayu Wicaksono Dewi Setiyawati Dewi Setiyawati Dyah Anggita, Sharazita Eli Pujastuti, Eli Ema Utami Ermawan, Bagas Restya Fahlevie, Reychandra Tristan Faisal Salim, Muhammad Fauzan Baehaqi Ikhsan Habib Kusuma Inggar Adi Prasetyo Kamarudin Kamarudin Kusuma Wardana, Raka M Iqbal Novananda Sugiharto Marsel Pratama, Aldino Mas’ud Mahfudz Darmawan, Muhammad Nugroho, Bagas Julio Nur Aini NUR AINI Nurcahyo, Narwanto Oktafria, Fikri Pamungkas, Adji Surya Pramudya Hidayat, Muhammad Rifqi Rakarahayu Putri, Raniya Ramadhan Bima Saputra Restu Agung, Akmal Fauzan Rizal Gibran Aldrin Pratama Rizki Gunawan, Rizki Salamun Sukriandi Santoso, Riki Adi Subektiningsih Subektiningsih Sugiharto, M Iqbal Novananda Susanti, Rahmadhani Tofa Nurcholis, Muhammad Uray Nur Khadijah Uyock Anggoro Saputro Wahyu Hidayat Wardhana, Raditya Wulandari, Irma Rofni Yoga Pristyanto Zurni Laila