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All Journal JUSIFO : Jurnal Sistem Informasi Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi Sistemasi: Jurnal Sistem Informasi Briliant: Jurnal Riset dan Konseptual Seminar Nasional Teknologi Informasi Komunikasi dan Industri INOVTEK Polbeng - Seri Informatika Jurnal Teknologi Sistem Informasi dan Aplikasi JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Tematik : Jurnal Teknologi Informasi Komunikasi Jurnal Teknik Informatika (JUTIF) Jurnal Computer Science and Information Technology (CoSciTech) Jurnal Pendidikan dan Teknologi Indonesia CONSEN: Indonesian Journal of Community Services and Engagement
Muttakin, Fitriani
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Religion Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Mathematics Physics Social Sciences Other

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Analisis Tingkat Kepuasan Pengguna Layanan Aplikasi Pemeliharaan Arsip Keluarga dengan Metode End User Computing Satisfaction Nur Sarifudin, Muhammad; Muttakin, Fitriani
Seminar Nasional Teknologi Informasi Komunikasi dan Industri 2023: SNTIKI 15
Publisher : UIN Sultan Syarif Kasim Riau

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Abstract

Layanan Aplikasi Pemeliharaan Arsip Keluarga merupakan suatu sistem layanan berbasis situs halaman yang diluncurkan Dinas Perpustakaan dan Kearsipan kota Pekanbaru untuk memfasilitasi masyarakat dalam melakukan aktivitas kearsipan serta perawatan terhadap arsip pribadi. Suatu sistem dinyatakan berkualitas jika mampu memberikan kepuasan terhadap penggunanya. Kepuasan ini tentu memberikan dampak positif terutama dalam hal penilaian untuk peningkatan kualitas layanan. Dengan menggunakan metode End Using Computing Satisfaction, berisikan variabel isi konten, akurasi, bentuk, kemudahan penggunaan, dan waktu. Maka tujuan penelitian ini yakni, guna mengukur tingkat kepuasan pengguna sistem LAPAK. Teknik pengumpulan data yang digunakan adalah dengan cara menyebarkan kuesioner kepada 70 responden pengguna sistem. Berdasarkan hasil yang dianalisis, diterimalah 2 hipotesis yakni Isi dan Waktu. Sedangkan 3 hipotesis lainnya yang ditolak yaitu Akurasi, Bentuk, dan Kemudahan Penggunaan. Hasil yang diperoleh melalui analisis SEM adalah tergolong cukup baik dengan nilai korelasi R-Square sebesar 61,8% untuk variabel kepuasan pengguna. Kata kunci: EUCS, kepuasan, LAPAK, SEM-PLS
Pengelompokan Data Pelayanan Berbasis Density Based Learning Putri, Amanda Iksanul; Marsal, Arif; Muttakin, Fitriani
Seminar Nasional Teknologi Informasi Komunikasi dan Industri 2023: SNTIKI 15
Publisher : UIN Sultan Syarif Kasim Riau

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Abstract

Pelayanan publik sebagaimana dimaksud dalam Undang-Undang RI Nomor 25 Tahun 2009 adalah setiap tindakan yang dilakukan dalam rangka memenuhi kebutuhan mendasar yang sejalan dengan hak asasi setiap warga negara dan penduduk atas produk, jasa, dan pelayanan administratif yang ditawarkan oleh penyedia layanan terkait dengan pelayanan publik. Kepuasan masyarakat yang tepat diperlukan untuk meningkatkan kualitas pelayanan. Dalam penelitian ini jawaban dari daerah yang mempunyai pola kasus yang sebanding dikelompokkan dengan menggunakan teknik clustering guna dijadikan sebagai tolak ukur standar pelayanan publik yang diberikan.  Algoritma DBSCAN diterapkan pada penelitian ini menggunakan 102 data dari masyarakat yang melakukan pelayanan di Kantor Camat Rumbai Timur untuk mendapatkan hasil klasterisasi. Dengan nilai Eps berkisar antara 3,0 hingga 4,0 dan nilai Minpts 7 dan 8, total uji coba dilakukan sebanyak 22 kali. Pada penelitian ini diperoleh satu cluster disetiap percobaan, dimana cluster tersebut mengelompokkan masyarakat puas terhadap pelayanan yang dilakukan di Kantor Camat Rumbai Timur. Hal tersebut terbukti melalui perhitungan sekala likert dimana semua variable pada data mendapatkan sekala puas dari masyarakat.
Implementasi Metode WASPAS Pada Sistem Pendukung Keputusan Penilaian Kinerja Perawat Terbaik Pratama, Arya Yendri; Muttakin, Fitriani; Permana, Inggih; Zarnelly, Zarnelly; Marsal, Arif
Journal of Information System Research (JOSH) Vol 5 No 3 (2024): April 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i3.5068

Abstract

Hospitals are health service institutions that provide various services to the community, including inpatient, outpatient and emergency care. Hospitals as health service institutions require optimal nurse performance in providing quality services to patients. At XYZ Hospital, assessments and calculations are still carried out manually, often experiencing difficulties because in carrying out the assessment all the calculation data is carried out one by one, resulting in large errors and taking quite a long time to obtain the decision results and it is difficult to carry out rankings due to frequent assessment forms. scattered. There are 14 criteria for assessing nurse performance, namely loyalty/loyalty, work performance, responsibility, obedience/discipline, honesty, cooperation, communication, knowledge, competency I nurse (PK I), competency II nurse (PK II), competency III nurse (PK III), the presence of hand washing in the room, the quality of the work carried out by the person concerned, and the availability of ready-to-use facilities & infrastructure for the next shift. To obtain accurate performance assessment results, a decision support system was created using the WASPAS method. The WASPAS method is said to be appropriate for selecting the best nurses because it is ranked based on specified criteria values. It is hoped that the research carried out will help obtain effective results. In this research, the results obtained were that the best nurse at XYZ Hospital was the alternative with a score of 50,038 in the name of EET.
Perbandingan Algoritma LSTM, Bi-LSTM, GRU, dan Bi-GRU untuk Prediksi Harga Saham Berbasis Deep Learning Tshamaroh, Muthia; Permana, Inggih; Salisah, Febi Nur; Muttakin, Fitriani; Afdal, M
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7252

Abstract

Stock price prediction is an important component in making investment decisions. This study aims to compare the performance of four deep learning models, namely LSTM, Bi-LSTM, GRU, and Bi-GRU, in predicting stock prices, in order to find the most optimal model for the implementation of an accurate stock price prediction system. Five years of historical data undergoes normalization, windowing, and is separated into training data, validation data, and test data. Model training is conducted with different settings of batch size, timestep, and three kinds of optimizers (Adam, SGD, RMSprop). Performance assessment employs MSE, RMSE, MAE, and R² measurements. The findings indicate that the Bi-GRU model utilizing Adam optimizer settings, a batch size of 8, and a timestep of 21 yields the highest performance, achieving an MSE of 0.0003, an RMSE of 0.0169, an MAE of 0.0129, and an R² of 0.9438. This model demonstrates a strong capability to identify intricate patterns and long-term temporal relationships, outperforming other models in accuracy. The results advocate for the establishment of a predictive system that aids investors and firms in making strategic decisions based on data.
Aspect-based Sentiment Analysis of Public Opinions on Integrated Islamic Schools using Lexicon based and Machine Learning Approaches Muttakin, Fitriani; Savra, Daffa Takratama
Sistemasi: Jurnal Sistem Informasi Vol 15, No 3 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i3.5848

Abstract

This study aims to examine public perceptions of Integrated Islamic Schools through aspect-based sentiment analysis by integrating Latent Dirichlet Allocation, Lexicon-Based approach, and Deep Neural Networks. LDA is employed to extract topic structures that represent the semantic context of public reviews, Lexicon Based method is used for sentiment analysis, while DNN infers sentiment orientation based on the extracted representations. This approach seeks to combine the strengths of probabilistic topic modeling and deep learning to obtain a more comprehensive understanding of public opinion. The analysis was conducted on a collection of 2,280 online reviews, which after preprocessing resulted in 1,438 reviews processed using the LDA–DNN combination. The results demonstrate that this approach is capable of identify in opinion dimensions in a more contextual manner and enhancing the interpretability of the analysis outcomes. Empirical evaluation shows that the proposed model achieved an accuracy of 63.89% for aspect classification and 93.06% for sentiment classification, outperforming the K-Means–LSA and K-Means–PCA approaches, which achieved 45.14% and 31.94% accuracy for aspect classification and 92.36% accuracy for sentiment classification, respectively. These findings confirm the superiority of probabilistic topic modeling in capturing complex semantic relationships and provide a methodological contribution to the development of sentiment analysis in the context of integrated Islamic education.
ANALISIS SENTIMEN ULASAN APLIKASI M-PASPOR MENGGUNAKAN NAIVE BAYES DAN SUPPORT VECTOR MACHINE Maheri, Rahman; Salisah, Febi Nur; Muttakin, Fitriani; Megawati, Megawati
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 1 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i1.5826

Abstract

ABSTRAKM-Paspor merupakan sebuah aplikasi pelayanan publik yang dirilis pada awal tahun 2022 lalu. Telah lebih dari satu juta unduhan aplikasi ini di Google Play Store. Dalam beberapa kasus, konten ulasan dan ulasan produk atau layanan dapat berbeda. Analisis umpan balik pengguna harus menjadi prioritas utama saat mengembangkan aplikasi. Penelitian ini dilakukan dengan tujuan untuk memahami persepsi pengguna aplikasi M-Paspor di Google Play Store, dengan cara mengidentifikasi sentimen positif dan negatif dalam ulasan mereka, serta Membandingkan kemampuan algoritma Support Vector Machine dan Naive Bayes dalam menghasilkan klasifikasi yang akurat dalam menganalisis sentimen tersebut. Metode penelitian melibatkan pengumpulan, preprocessing, pelabelan, dan klasifikasi 5.934 ulasan aplikasi M-Paspor di Google Play Store, dan penerapan algoritma Support Vector Machine dan Naive Bayes untuk mengklasifikasikan sentimen ulasan. Setelah menerapkan algoritma, tahap terakhir adalah pengujian evaluasi. Hasil penerapan algoritma klasifikasi, menunjukkan bahwa algoritma Support Vector Machine dapat mengklasifikasikan ulasan aplikasi M-Paspor dengan lebih akurat, dengan rata-rata 80,76% dibandingkan dengan 78,12% dari Naive Bayes. Temuan ini menunjukkan potensi Support Vector Machine dalam membantu pengembang M-Paspor meningkatkan kualitas layanan berdasarkan umpan balik pengguna. ABSTRACTM-Paspor is a public service application that was released in early 2022. There have been more than one million downloads of this application on the Google Play Store. In some cases, review content and product or service reviews may differ. Analyzing user feedback should be a top priority when developing apps. This research was conducted with the aim of understanding the perceptions of users of the M-Paspor application on the Google Play Store, by identifying positive and negative sentiments in their reviews, and comparing the ability of the Support Vector Machine and Naive Bayes algorithms to produce accurate classifications in analyzing these sentiments. The research method involves collecting, preprocessing, labeling, and classifying 5,934 reviews of the M-Paspor app on the Google Play Store, and applying the Support Vector Machine and Naive Bayes algorithms to classify the sentiment of the reviews. After applying the algorithm, the last stage is evaluation testing. The results of applying the classification algorithms, show that the Support Vector Machine algorithm can classify M-Paspor app reviews more accurately, with an average of 80.76% compared to 78.12% from Naive Bayes. These findings demonstrate the potential of Support Vector Machine in helping M-Paspor developers improve service quality based on user feedback.
Co-Authors Ahsyar, Tengku Khairil Andrika, Nadila Angraini Angraini Anjani, Yulia Merry Ardiansah, Yoga Ariansah, Rizky Arif Marsal Auliadi, Maulana Dermawan, Tri Efendi, Harisman Eki Saputra Febi Nur Salisah Fitri Amillia Hamzah, Muhammad Luthfi Inggih Permana Intan, Sofia Fulvi Jazman, Muhammad M. Afdal Maheri, Rahman Megawati - Mona Fronita, Mona Mubarak MR, Najmuddin Muhammad Fadli Nabillah, Putri Nur Sarifudin, Muhammad Pratama, Arya Yendri Pristiawati, Andani Putri Putri, Amanda Iksanul Rachmawati, Dyana Rahmawati, Rahmawati Rahmawita Munzir, Medyantiwi Sabillah, Dian Ayu Safrudin, Muhammad Salsabila, Salsabila Sarbaini Sarbaini Savra, Daffa Takratama Siregar, Muslim Putra Perdana Siti Monalisa Syahri, Alfi Tasia, Ena Tshamaroh, Muthia Zarnelly Zarnelly