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All Journal Jurnal Edik Informatika : Penelitian Bidang Komputer Sains dan Pendidikan Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) IT JOURNAL RESEARCH AND DEVELOPMENT INOVTEK Polbeng - Seri Informatika JITK (Jurnal Ilmu Pengetahuan dan Komputer) PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Digital Zone: Jurnal Teknologi Informasi dan Komunikasi JOURNAL OF SCIENCE AND SOCIAL RESEARCH Building of Informatics, Technology and Science Jurnal J-PEMAS JAIA - Journal of Artificial Intelligence and Applications Jurnal Pemberdayaan Sosial dan Teknologi Masyarakat SATIN - Sains dan Teknologi Informasi Bulletin of Social Informatics Theory and Application Neraca: Jurnal Ekonomi, Manajemen dan Akuntansi JEBD The Indonesian Journal of Computer Science INOVTEK Polbeng - Seri Informatika
Susandri, Susandri
Universitas Lancang Kuning
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Social Sciences

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Sistem Pencarian Obat pada Apotek Ratna Yulia; Susandri Susandri
SATIN - Sains dan Teknologi Informasi Vol 4 No 2 (2018): SATIN - Sains dan Teknologi Informasi
Publisher : STMIK Amik Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (374.718 KB) | DOI: 10.33372/stn.v4i2.384

Abstract

Apotek merupakan sebuah usaha yang menangani peracikan dan penjualan obat yang banyak tersedia di setiap daerah, salah satunya di Riau. Hingga saat ini untuk mendapatkan informasi keberadaaan obat yang dibeli masyarakat harus mengunjungi ke setiap apotek dimana akan memerlukan waktu dan biaya tambahan untuk mencarinya. Oleh karena itu perlu dibangunnya sistem pencarian obat pada apotek dengan memfasilitasi pengguna untuk mendapatkan informasi apotek yang menjual obat berdasarkan kata kunci nama obat, jenis obat dan nama apotek. Metode pengembangan sistem yang digunakan penelitian ini yaitu waterfall. Dengan adanya sistem tersebut dapat membantu masyarakat dalam mencari obat-obatan yang dibutuhkan tanpa mendatangi apotek langsung.
Perancangan Sistem Indek Kepuasan Pelanggan Pemakaian Lapangan Futsal Menggunakan Metode Scoring System Karpen; Anwar Siregar; Susandri
SATIN - Sains dan Teknologi Informasi Vol 7 No 1 (2021): SATIN - Sains dan Teknologi Informasi
Publisher : STMIK Amik Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (789.829 KB) | DOI: 10.33372/stn.v7i1.705

Abstract

Kepuasan pelanggan akan menjadi prioritas utama, jika kegiatan yang dilakukan berupa jasa atau penyedia layanan. Selama ini pelanggan merasakan layanan yang diberikan oleh Elang Futsal kurang memuaskan, seperti kondisi lapangan dan kamar mandi yang kotor. Untuk menjaga agar para pelanggan terus memakai lapangannya, Elang Futsal harus memperbaiki layanannya. Untuk itu perlu dibuat perancangan sistem yang bisa mengetahui indek kepuasan pelanggan. Indek kepuasan pelanggan merupakan isian kuisioner mengenai layanan yang ada di Elang Futsal berupa pertanyaan yang diberikan kepada pelanggan sebanyak 35 orang. Untuk memperoleh indek kepuasan pelanggan digunakan metode Scoring System, yaitu metode yang memberikan penilaian berupa skala skor, sebagai norma pembanding mengenai harapan dan kenyataan yang sesuai katagorinya, tidak puas, puas dan sangat puas. Hasil pengujian dengan aplikasi sistem, indek kepuasan pelanggan kategori tidak puas dengan nilai kecil 49, puas kecil 71 dan sangat puas kecil 105. Hasil penghitungan tersebut bisa menjadi pedoman manajemen Elang Futsal untuk meningkatkan pelayanannya.
SENTIMENT LABELING AND TEXT CLASSIFICATION MACHINE LEARNING FOR WHATSAPP GROUP Susandri Susandri; Sarjon Defit; Muhammad Tajuddin
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 9 No. 1 (2023): JITK Issue August 2023
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v9i1.4201

Abstract

The use of WhatsApp Group (WAG) for communication is increasing nowadays. WAG communication data can be analyzed from various perspectives. However, this data is imported in the form of unstructured text files. The aim of this research is to explore the potential use of the SentiwordNet lexicon for labeling the positive, negative, or neutral sentiment of WAG data from "Alumni94" and training and testing it with machine learning text classification models. The training and testing were conducted on six models, namely Random Forest, Decision Tree, Logistic Regression, K-Nearest Neighbors (KNN), Linear Support Vector Machine (SVM), and Artificial Neural Network. The labeling results indicate that neutral sentiment is the majority with 7588 samples, followed by 324 negative and 1617 positive samples. Among all the models, Random Forest showed better precision and recall, i.e., 83% and 64%. On the other hand, Decision Tree had slightly lower precision and recall, i.e., 80% and 66%, but exhibited a better f-measure of 71%. The accuracy evaluation results of the Random Forest and Decision Tree models showed significant performance compared to others, achieving an accuracy of 89% in classifying new messages. This research demonstrates the potential use of the SentiwordNet lexicon and machine learning in sentiment analysis of WAG data using the Random Forest and Decision Tree models
The Readiness Analysis of Smart School Implementation Using Technology Readiness Index to Support Smart City Implementation M. Khairul Anam; Indra Prayogo; Susandri; Yoyon Efendi; Erlin; nurjayadi
Bulletin of Social Informatics Theory and Application Vol. 6 No. 2 (2022)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v6i2.507

Abstract

Smart Schools have been widely applied in several schools within the scope of education and services as they are being encouraged to support Smart City. Smart Schools is a school concept utilizing information technology used in the teaching and learning process in the class and school administration. One of the schools in Pekanbaru City that will implement intelligent schools in Junior High School 17 Pekanbaru. Building smart schools themselves is adequate infrastructure such as servers, labor, and integrated systems and the readiness of schools and students to implement Smart Schools in the future. Therefore, to determine the readiness level of prospective users of the Smart Schools concept, the technology readiness index (TRI) method with four personality variables; optimism, innovativeness, discomfort, and insecurity. The purpose of this research was to find out the readiness index of prospective users in the implementation of Smart Schools and see what factors need to be improved from the readiness of prospective users. This research was expected to help Junior High School 17 prepare schools to become Smart Schools to support smart city implementation in Pekanbaru
Mengukur Tingkat Pelayanan Pajak Bumi dan Bangunan Menggunakan Metode Service Quality (Studi Pada Dinas Pendapatan Daerah Kota Pekanbaru) Karpen, Karpen; Rokib, Amul; Susandri, Susandri
Building of Informatics, Technology and Science (BITS) Vol 2 No 1 (2020): June 2020
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (358.429 KB) | DOI: 10.47065/bits.v2i1.262

Abstract

The Regional Revenue Service (DISPENDA) of Pekanbaru City is a Regional Work Unit (SKPD) which is given the authority to collect and manage local taxes as well as the Regional Financial Management Officer (PPKD). As a tax collection SKPD that relies heavily on trust and public service, DISPENDA is demanded to provide optimal services by always prioritizing the quality of service so that taxpayers will feel satisfied and comfortable, especially for land and building tax (PBB). At present there are still complaints from taxpayers for services provided by DISPENDA, which results in laziness to pay taxes so that there are many tax arrears. For this reason, it is necessary to measure the level of service so that they can find out the complaints of taxpayers and DISPENDA employees can improve the quality of their services. This study uses the Servqual (Service Quality) method which includes variables or dimensions in the form of physical evidence (tangibles), reliability (reliability), responsiveness (responsiveness), assurance (assurance), and empathy (empathy). The results of the test show that each variable gets a positive customer indicator value, namely the physical evidence indicator = 3.82, guarantee = 3.61, empathy = 3.58, responsiveness = 3.66 and reliability = 3.67 from the standard value set. Variable physical evidence (tangibles), has a significant positive influence on satisfaction of taxpayers with an indicator value = 3.82, while the variables of empathy (reliability), reliability (responsiveness), and guarantee (assurance) effect but not significant impact on taxpayer services. This measurement of service level to provide public satisfaction in carrying out its obligations and increase land and building tax revenues for DISPENDA
The Optimizing Sales Strategies to Address Excessive Stock Accumulation: A Data Mining Approach Susandri; Muhammad Arief Solihin; Hamdani; Asparizal
JAIA - Journal of Artificial Intelligence and Applications Vol. 4 No. 1 (2024): JAIA - Journal of Artificial Intelligence and Applications
Publisher : STMIK Amik Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33372/jaia.v4i1.1110

Abstract

The Two Pelita Weaving Business has recorded significant sales in the weaving industry, despite facing challenges in managing product stock due to the accumulation of excess stock caused by a lack of customer interest. This study employs data mining techniques, specifically the Association Rule and Apriori algorithms, to analyze sales patterns. The analysis results using Python and Orange Data Mining showed consistency in the relationship between Siku Keluang Weaving and Pucuk Rebung Weaving products, with high occurrence rates of purchase patterns (11.74% and 10%, respectively). High confidence levels with Python at 96.36% and Orange Data Mining at 99.1% indicate that customers who purchase Siku Keluang Weaving are also likely to purchase Pucuk Rebung Weaving products.
Sentimen Pengguna Aplikasi BRImo: Kinerja Algoritma Support Vector Machine, Naive Bayes, dan Adaboost Susandri; Yurnalis; Edwar Ali; Susanti; Asparizal
SATIN - Sains dan Teknologi Informasi Vol 9 No 2 (2023): SATIN - Sains dan Teknologi Informasi
Publisher : STMIK Amik Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33372/stn.v9i2.1057

Abstract

Dalam konteks perkembangan industri perbankan yang semakin maju, pemanfaatan teknologi modern menjadi faktor kunci untuk meningkatkan kualitas layanan dan memenangkan persaingan di era digital. Bank Rakyat Indonesia (BRI) memikat perhatian masyarakat melalui peluncuran aplikasi perbankan seluler, BRImo. Namun Bank ini perlu meraih pandangan dan pengalaman nasabah terhadap aplikasi mobile banking untuk meningkatkan kualitas pelayanan. Penelitian ini memiliki tujuan untuk menganalisis ulasan pengguna BRImo sebagai objek penelitian. Komparasi dilakukan antara algoritma Support Vector Machine (SVM), Naive Bayes (NB), dan Adaboost dalam mengolah data teks. Evaluasi dilakukan berdasarkan tingkat akurasi, presisi, recall, dan nilai F1-score. Hasil penelitian menunjukkan bahwa algoritma SVM memberikan kinerja terbaik dalam mengklasifikasikan tanggapan masyarakat terhadap aplikasi BRImo, dengan tingkat akurasi sebesar 90,4%, presisi 90,8%, recall 90%, dan nilai F1-score 90,3%. Sebagai perbandingan, algoritma Adaboost memberikan nilai terendah dengan tingkat akurasi sebesar 87%, presisi 87,2%, recall 86,8%, dan nilai F1-score 86,9%.
Analysis Of The Impact Of Hotel Labersa Service Quality On Guest Satisfaction And Loyalty: A Case Study Using Path Analysis Handayani, Risna; Rahman, Bilhaki; Susandri, Susandri
Neraca: Jurnal Ekonomi, Manajemen dan Akuntansi Vol. 3 No. 2 (2024): Neraca: Jurnal Ekonomi, Manajemen dan Akuntansi
Publisher : Neraca: Jurnal Ekonomi, Manajemen dan Akuntansi

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

Abstract

Dalam penelitian yang membahas kualitas layanan terhadap kepuasan dan loyalitas tamu di Hotel Labersa, dimulai dengan menganalisis belum optimalnya pelayanan di hotel tersebut. Penelitian ini mencakup kelima dimensi SERVQUAL: bukti fisik, kehandalan, daya tanggap, jaminan, dan empati yang dipelajari untuk menentukan pengaruh langsung dan tidak langsungnya terhadap kepuasan tamu dan pada akhirnya loyalitas. Dalam penelitian ini, 300 tamu hotel memberikan pandangan mereka melalui kuesioner terstruktur, data yang dikumpulkan kemudian dianalisis menggunakan SEM. Temuan menunjukkan bahwa kelima dimensi tersebut secara signifikan mempengaruhi persepsi tamu terhadap kepuasan, yang pada gilirannya mempengaruhi loyalitas. Empati dan daya tanggap memiliki roles terkuat dalam mengungkapkan dimensi-dimensi kunci kepuasan tamu, menyoroti pentingnya interaksi pribadi dan timbal balik dalam proses ini. Hasil ini menegaskan pentingnya kualitas layanan dalam industri perhotelan dan sisanya, dan mendukung gagasan bahwa peningkatan dimensi-dimensi ini akan meningkatkan loyalitas. Ini memberikan panduan untuk manajemen hotel dalam perbaikan proses dan pelayanan mereka, mencatat fokus pada interaksi staf dan tamu untuk diutamakan. Selain itu, saran untuk penelitian lebih lanjut untuk meninjau pengaruh teknologi pada personalisasi layanan karena tren modern saat ini.
Peningkatan Kolaborasi Generasi Z Di Lingkungan Pendidikan Dengan Menerapkan SIM Berbasis Cloud Computing Desinawati, Desinawati; Feby Budi Setiawan; Tutut Setiarini; Susandri, Susandri
Jurnal Ekonomi dan Bisnis Digital Vol. 2 No. 2 (2024): Oktober - Desember
Publisher : CV. ITTC INDONESIA

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

Abstract

The research was to analyze the relationship between SIM, Cloud Computing and Generation Z collaboration in the school environment, with a focus on SMK Muhammadiyah 1 Pekanbaru. The research method applied is quantitative, it is done by collecting data on answers filled in by students in answering the distributed questionnaires. Likewise for data collected with teacher respondents. The analysis results show that the implementation of a cloud-based management information system has succeeded in increasing collaboration between students and between students and teachers, with a measurable increase in collaboration reaching 85%. These findings indicate that the integration of cloud computing technology in educational systems not only facilitates better communication, but also supports the development of collaborative skills of generation Z. This research recommends further implementation of cloud-based systems to improve the efficiency and effectiveness of learning in educational institutions.
The Mitigating Overfitting in Sentiment Analysis Insights from CNN-LSTM Hybrid Models Susandri, Susandri; Zamsuri, Ahmad; Nasution, Nurliana; Efendi, Yoyon; Alwan, Hiba Basim
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 24 No 2 (2025)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i2.4742

Abstract

This study aims to improve sentiment analysis accuracy and address overfitting challenges in deep learning models by developing a hybrid model based on Convolutional Neural Networks and Long Short-Term Memory Networks. The research methodology involved multiple stages, starting with preprocessing a dataset of 5,456 rows. This process included removing duplicate data, empty entries, and neutral sentiments, resulting in 2,685 usable rows. To overcome data quantity limitations, data augmentation expanded the training dataset from 2,148 to 10,740 samples. Data transformation was carried out using tokenization, padding, and embedding techniques, leveraging Word2Vec and GloVe to produce numerical representations of textual data. The hybrid model demonstrated strong performance, achieving a training accuracy of 99.51%, validation accuracy of 99.25%, and testing accuracy of 87.34%, with a loss value of 0.56. Evaluation metrics showed precision, recall, and F1-Score values of 86%, 87%, and 86%, respectively. The hybrid model outperformed individual models, including Convolutional Neural Networks (70% accuracy) and Long Short-Term Memory Networks (81% accuracy). It also surpassed other hybrid models, such as the multiscale Convolutional Neural Network-Long Short-Term Memory Network, which achieved a maximum accuracy of 89.25%. The implications of this study demonstrate that the hybrid model based on Convolutional Neural Networks and Long Short-Term Memory Networks effectively improves sentiment analysis accuracy while reducing the risk of overfitting, particularly in small or imbalanced datasets. Future research is recommended to enhance data quality, adopt more advanced embedding techniques, and optimize model configurations to achieve better performance.
Co-Authors -, Henry Adi Wansyah Agustin Ahmad Zamsuri, Ahmad Alwan, Hiba Basim Anam, M Khairul Anwar Siregar Asparizal Asparizal, Asparizal Bakri Nasution, Feldiansyah Bimby, Novia Putri Bosker Sinaga Delpa Mario Desinawati, Desinawati Edwar Ali Efrizoni, Lusiana Endardhi, Azmi Rahmat Erlin Fajrizal Fajrizal, Fajrizal Feby Budi Setiawan Feldiansyah, Feldiansyah Fristi Riandari Hamdani Handayani, Risna Henry - Herwin -, Herwin Herwin Herwin, Herwin Hiba Basim Alwan Hidaya Spitri Indra Prayogo Indra Prayogo Istithoatun Kholishoh Karpen Khusaeri Andesa Koko Harianto Koko Harianto, Koko Mardainis Mardainis -, Mardainis Melgisaputra Dwi Nanda Melgisaputra Dwi Nanda, Melgisaputra Dwi Muhammad Arief Solihin MUHAMMAD TAJUDDIN Nora Lizarti Nurjayadi Nurjayadi Nurliana Nasution Nurliana Nasution, Nurliana Pane, Eddissyah Putra Rahmaddeni Rahmaddeni Rahman, Bilhaki Rahmiati Rahmiati Rahmiati Ramadhani, Maya Ratna Yulia Rokib, Amul Salsabila Rabbani Sandi Saputra Sandi Saputra Sari Herlina Sarjon Defit Sarjon Defit Sumijan , Sumijan SUSANTI SUSI ERLINDA Triyani Arita Fitri Tutut Setiarini Ulfah, Aniq Noviciate Wansyah, Adi Yoyon Efendi Yoyon Efendi Yulia, Ratna Yurnalis Zamzuri, Ahmad