Article Per Year (5 Year)
p-Index From 2021 - 2026
12.643
P-Index
This Author published in this journals
All Journal AGRIVITA, Journal of Agricultural Science Jurnal Informatika dan Teknik Elektro Terapan JurTI (JURNAL TEKNOLOGI INFORMASI) Antivirus : Jurnal Ilmiah Teknik Informatika Jurnal ICT : Information Communication & Technology JATI (Jurnal Mahasiswa Teknik Informatika) JISA (Jurnal Informatika dan Sains) JUSTIN (Jurnal Sistem dan Teknologi Informasi) Journal of Renewable Energy, Electrical, and Computer Engineering PENA ABDIMAS : Jurnal Pengabdian Masyarakat BULLET : Jurnal Multidisiplin Ilmu AMMA : Jurnal Pengabdian Masyarakat Jurnal Sistem Informasi dan Manajemen Jurnal Kecerdasan Buatan dan Teknologi Informasi Spektra : Jurnal Ilmu-ilmu Sosial Educommunity Jurnal Pengabdian Masyarakat As-Sulthan Journal of Education Jurnal Komtika (Komputasi dan Informatika)
Irfan Ali, Irfan
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Humanities Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Medicine & Pharmacology Nursing Public Health Social Sciences Other

Published : 105 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 105 Documents
Search

 7   8   9   10   11 
KOMPARASI ALGORITMA REGRESI LINEAR DAN BACKPROPAGATION NEURAL NETWORK PADA SISTEM PREDIKSI HARGA SAHAM BERBASIS WEBSITE Setiawan, Riyan; Purnamasari, Ade Irma; Ali, Irfan; Rohmat, Cep Lukman; Dwilestari, Gifthera
Jurnal Informatika dan Teknik Elektro Terapan Vol. 14 No. 1 (2026)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v14i1.8468

Abstract

Penelitian ini bertujuan untuk membandingkan performa algoritma Regresi Linear dan Backpropagation Neural Network dalam memprediksi harga saham PT Astra Agro Lestari serta mengimplementasikannya ke dalam sistem prediksi berbasis web. Data historis saham dari Kaggle digunakan dengan variabel previous, high, low sebagai input dan close sebagai target. Pengembangan sistem menggunakan model Waterfall melalui tahapan analisis kebutuhan, desain, implementasi, pengujian, dan analisis komparatif. Pelatihan model dilakukan menggunakan Scikit-learn untuk Regresi Linear dan TensorFlow/Keras untuk Backpropagation Neural Network, dengan preprocessing MinMaxScaler dan pembagian data latih dan uji sebesar 80:20. Evaluasi model menggunakan Root Mean Squared Error (RMSE) dan Mean Absolute Error (MAE). Hasil pengujian menunjukkan BPNN lebih akurat dengan RMSE 26.81 dan MAE 19.01, dibandingkan Regresi Linear dengan RMSE 45.11 dan MAE 29.56. Sistem web berhasil menampilkan prediksi otomatis, grafik komparatif, dan evaluasi error secara real-time.
ANALISIS SENTIMEN ULASAN APLIKASI BANK JAGO MENGGUNAKAN SUPPORT VECTOR MACHINE DAN NEURAL NETWORK Mariyani, Dinda; Irma Purnamasari, Ade; Ali, Irfan; Nurdiawan, Odi; Nurdiawan, Rudi
Jurnal Informatika dan Teknik Elektro Terapan Vol. 14 No. 1 (2026)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v14i1.8775

Abstract

Abstrak. Pertumbuhan layanan perbankan digital di Indonesia menjadikan ulasan pengguna pada Google Play Store sebagai sumber penting untuk mengevaluasi kualitas aplikasi, termasuk Bank Jago. Namun, ulasan tersebut bersifat tidak terstruktur, informal, dan mengandung noise sehingga menyulitkan analisis sentimen. Penelitian ini bertujuan memberikan gambaran objektif kecenderungan opini pengguna serta membandingkan kinerja algoritma Support Vector Machine (SVM) dan Neural Network (MLPClassifier). Sebanyak 10.000 ulasan dikumpulkan melalui scraping dan direduksi menjadi 7.946 ulasan setelah penghapusan duplikasi. Data diproses melalui tahapan preprocessing meliputi cleaning, case folding, normalisasi slang, tokenisasi, stopword removal, dan stemming. Pelabelan sentimen dilakukan menggunakan lexicon InSet, sedangkan ekstraksi fitur menggunakan CountVectorizer berbasis Bag-of-Words. Hasil penelitian menunjukkan bahwa SVM memperoleh akurasi tertinggi sebesar 91,2%, lebih unggul dibandingkan Neural Network dengan akurasi 89,8%. Temuan ini menegaskan bahwa pemilihan preprocessing dan representasi fitur yang tepat berperan penting dalam meningkatkan performa analisis sentimen pada ulasan aplikasi perbankan digital. Abstract. The growth of digital banking services in Indonesia has made user reviews on the Google Play Store an important source for evaluating application quality, including Bank Jago. However, these reviews are unstructured, informal, and noisy, creating challenges for sentiment analysis. This study aims to provide an objective overview of user sentiment and to compare the performance of Support Vector Machine (SVM) and Neural Network (MLPClassifier). A total of 10,000 reviews were collected through scraping and reduced to 7,946 reviews after duplicate removal. The data were processed through preprocessing stages including cleaning, case folding, slang normalization, tokenization, stopword removal, and stemming. Sentiment labeling was conducted using the InSet lexicon, while feature extraction employed a Bag-of-Words approach with CountVectorizer. The results show that SVM achieved the highest accuracy of 91.2%, outperforming the Neural Network model with 89.8%. These findings highlight the importance of appropriate preprocessing and feature representation for improving sentiment analysis performance in digital banking application reviews.
ANALISIS SENTIMEN ULASAN PENGGUNA APLIKASI FLO DI GOOGLE PLAY STORE DENGAN MENGGUNAKAN ALGORITMA NAIVE BAYES Kurniawati, Eti; Irma Purnamasari, Ade; Ali, Irfan; Kurniawan, Rudi; Nurdiawan, Odi
Jurnal Informatika dan Teknik Elektro Terapan Vol. 14 No. 1 (2026)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v14i1.8776

Abstract

Abstrak. Penelitian ini bertujuan untuk menganalisis sentimen ulasan pengguna aplikasi Flo pada Google Play Store menggunakan algoritma Multinomial Naive Bayes. Flo merupakan aplikasi mobile health (mHealth) populer yang digunakan untuk memantau siklus menstruasi dan kesehatan reproduksi. Data dikumpulkan melalui web scraping dan menghasilkan 10.000 ulasan yang setelah pembersihan menjadi 6.908 data valid. Proses pra-pemrosesan meliputi case folding, cleaning, normalisasi, tokenisasi, stopword removal, dan stemming menggunakan Sastrawi. Pelabelan sentimen dilakukan secara semi-otomatis berbasis lexicon InSet dan rating. Ekstraksi fitur menggunakan CountVectorizer menghasilkan representasi Bag-of-Words sebagai input model. Hasil evaluasi menunjukkan bahwa algoritma Naive Bayes mencapai akurasi sebesar 73,6% dengan nilai precision, recall, dan F1-score yang seimbang pada tiga kelas sentimen. Temuan ini menunjukkan bahwa Naive Bayes efektif digunakan dalam mengolah ulasan teks pendek dan informal berbahasa Indonesia. Penelitian ini berkontribusi dalam pemanfaatan machine learning untuk analisis sentimen aplikasi mHealth serta menyediakan wawasan yang dapat digunakan pengembang untuk meningkatkan kualitas layanan aplikasi Flo. Abstract. This study aims to analyze user reviews of the Flo application on Google Play Store using the Multinomial Naive Bayes algorithm. Flo is a popular mobile health (mHealth) application for tracking menstrual cycles and reproductive health. Data were collected using web scraping, obtaining 10,000 initial reviews, with 6,908 valid reviews after cleaning. Preprocessing included case folding, cleaning, normalization, tokenization, stopword removal, and stemming using Sastrawi. Sentiment labeling was performed semi-automatically using the InSet lexicon and rating-based rules. Feature extraction used CountVectorizer with the Bag-of-Words approach. The evaluation shows that Naive Bayes achieved an accuracy of 73.6% with balanced precision, recall, and F1-score across sentiment classes. These results indicate that Naive Bayes is effective for processing short and informal Indonesian text reviews. This research contributes to the application of machine learning in mHealth sentiment analysis and provides insights for developers to improve the quality of the Flo application.
TINJAUAN PUSTAKA: PERAN SEKOLAH ISLAM DALAM MEMBENTUK KESADARAN POLITIK DAN KEWARGANEGARAAN SISWA Fitria, Lailatul; Ali, Irfan; Putriana, Eka; Khairul Anam, Rifqi
As-Sulthan Journal of Education Vol. 3 No. 1 (2026): Januari
Publisher : As-Sulthan Journal of Education

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

Abstract

Islamic schools play a strategic role in shaping students’ political and civic awareness amid the increasingly complex socio-political dynamics of Indonesian society. This article aims to systematically examine the role of Islamic schools in cultivating students’ political awareness and civic identity based on findings from previous studies. The method employed is a literature review, analyzing various scholarly sources, including books, journal articles, and policy documents related to Islamic education, civic education, and political education. The findings indicate that Islamic schools contribute significantly through the integration of the national curriculum with Islamic values, particularly within Civic and Pancasila Education, Islamic Religious Education, as well as extracurricular activities and school culture. Islamic values such as justice (al-‘adl), trust (amanah), deliberation (shura), responsibility, tolerance, and social concern serve as ethical foundations in shaping students’ moderate and democratic political attitudes. Moreover, teachers’ roles as role models, participatory school environments, and students’ involvement in school organizations further strengthen the formation of civic awareness. Nevertheless, the review also identifies several challenges, including limited teacher competence, insufficient curriculum integration, and the influence of globalization. Therefore, strengthening political education grounded in Islamic values is essential to developing a generation of Muslims with strong character, political awareness, and responsibility within democratic life.
Deep Learning-Based Consumer Preference Analysis for Batik Packaging Design Using Convolutional Neural Networks Wahyudin, Edi; Bahtiar, Agus; Ali, Irfan; Nurhidayat, Muhammad
JISA(Jurnal Informatika dan Sains) Vol 8, No 2 (2025): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v8i2.2494

Abstract

Packaging design plays an essential role in shaping consumers’ first impressions of a product, particularly in the batik industry, where cultural meaning and visual identity are deeply intertwined. This study aims to explore how a Convolutional Neural Network (CNN) can help identify consumer preferences toward various batik packaging designs. The dataset consists of real packaging from local SMEs as well as prototype designs created specifically for this research, incorporating variations in motifs, colors, and structural formats. All images were standardized and normalized to ensure consistency before being processed by the CNN model. The architecture consists of several convolutional layers, pooling layers, and fully connected layers, with dropout applied to reduce overfitting. Model training was conducted using the Adam optimizer and the sparse categorical cross-entropy loss function. The results demonstrate that the model achieved a testing accuracy of 92.51%. Stable performance across precision, recall, and F1-score indicates that the CNN effectively captures visual patterns associated with consumer appeal. These findings highlight the potential for batik SMEs to utilize deep learning as a decision-support tool, enabling them to design packaging that is more appealing, relevant, and aligned with contemporary consumer preferences.
Co-Authors Abdul Rohim, Adi Nur Abdul Rosid, Rizal Ade Irma Adella, Luthfiyyah Iffah Adi Supriyatna Adinata, Adinata Ahmad Faqih Ahmad Jaelani Al-Maulid, Hisyam Aldiyansyah, Aldiyansyah Alfin Maulana Alfudola, Mahfudz Alkatiri, Nazwa Alvianatinova, Via Amalia, Rosnita Amer, Abdu Shobarudin Ana Amalia, Ana Andriyanti, Rina Annurfariz, Aditya Apriliana Janatu Marwa Aqlani, Zaheer Ahmed Arofah, Mila Asep Yoyo Wardaya Auliya, Suci Ayuningsih, Sri Az-Zahra, Asih Azrul, Ahmad Azzam, Ahmad Brohi, Sheeraz Aleem Burhanudin, Haris Dahri, Shahzad Hussain Dahri, Zakir Hussain Dahri, Zamin Hussain Dendy Indriya Efendi Destiawati, Deby Dewanty Rafu, Maria Dienwati Nuris, Nisa Dikananda, Arif Rinaldi Dikananda, Fatihanursari Efendi , Dendy Indriya Effendy, Dendy Indria ETI KURNIAWATI Fahreza, Rheznandya Faisal Adam, Faisal Faqih, Habib Fasa, Saefullah Fatmawati, Aisyah FAUZAN, AKMAL Fazari Hidayat, Nizar Fitria, Lailatul Gifthera Dwilestari Gitacahyani, Adisty Gunia, Euis Hadiyanto Hadiyanto Hagi Badra, Muhammad Hendiana, Hendiana Hermawan, Ramdan Hidayah, Freni Mega Hidayattullah, Rizky Huda, Irhamul Hurifiani, Alfia Ikbal, Ali Ilham, Mokhamad Indah Indah Indriya Efendi, Dendy Indriyan Dwi Kesuma, Adri Irma Purnama sari, Ade Irma Purnamasari , Ade Irma Purnamasari, Ade Julkarnaen, Agus Juwita, Ita Karbala, Syahid Kaslani Khalda Rifdan, Ghina Lana Sularto Lestari, Gifthera Dwi Listianto, Ahmad Bilal Lisyana, Zita Lukman Rohmat, Cep Mahdalena, Putri Ayu Mangrio, Abdul Ghafoor Mangrio, Munir Ahmed Mariyani, Dinda Martanto . Maulana, Ali Mayang Fadilah, Dewi Muhamad Basysyar , Fadhil Muharam, Arbi Adi Muharram, Akbar Muhimmatul ulya, Syilwa Mulyawan Nawang Wulan, Hidayah Nining Rahaningsih Nugraha, Rifqi Nugroho, Rizwar Adi Nur Alam, Alfian Nur Aziziah, Aldila Nurdiawan, Rudi Nurhidayat, Muhammad Nursaniah, Rini Nursatika Kusuma, Ines Odi Nurdiawan Oktaviani Putri , Farra Pajri, Riki Pardiana, Firda Prahara, Sukma Pratama, Denni Purnamasari, Ade Irma Putra Pratama, Aeri Putriana, Eka R, Nining Raafi, Muhammad Rahmi Safitri, Rahmi Ramanto, Aditiya Ramdani, Rizki Rayhan, Tubagus Muhammad Ridho Nugraha Rifa'i, Akhmad Rifqi Khairul Anam Rikiyashi, Afkan Rismala, Rismala Rizki Rinaldi, Ade Rizky Wulandhari, Putri Rodhiyana, Mu'allimah Rohman, Dede Rohmat, Cep Lukman Rosyd, Abdul Roziqin, Ahmad Khoirur Rudi Kurniawan Sadiyah, Ainur Rohimatus Salamah, Soviatus Saleem, Salman Saputra, Muhammad Sariah Sariah Setianingsih, Indri Setiawan, Riyan Shaikh, Irfan Ahmed Sholihin Fauzan, Aldi Sofialaela, Annisa Solihudin, Dodi Sri Widyastuti Suarna, Nana Sudrajat, Adi Suryana, Aldi Susana, Heliayanti Susana, Heliyanti Syahrul, Adis Tohidi, Edi Vina, Vina Wahyudin, Edi Windy Mardiyyah, Nita Wirdiyan, Farhan Azfa Wisnu Saputra, Adrian wiwied pratiwi, wiwied Yudhistira Arie Wijaya Yulistiano, Irena Zahrudin Zhahiran Herlambang, Prilanisa