Article Per Year (5 Year)
p-Index From 2021 - 2026
4.407
P-Index
This Author published in this journals
All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Jurnas Nasional Teknologi dan Sistem Informasi Sistemasi: Jurnal Sistem Informasi Dinamisia: Jurnal Pengabdian Kepada Masyarakat Jurnal Infomedia INTECOMS: Journal of Information Technology and Computer Science MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer EDUMATIC: Jurnal Pendidikan Informatika Jurnal Informasi dan Teknologi Community Engagement and Emergence Journal (CEEJ) International Journal of Electrical, Energy and Power System Engineering (IJEEPSE) ARSY : Jurnal Aplikasi Riset kepada Masyarakat Simpatik: Jurnal sistem Informasi dan Informatika Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Sistem Informasi dan Teknik Informasi Jurnal Teknik Informatika Journal of Applied Business and Technology PROCEEDING OF INTERNATIONAL CONFERENCE ON BUSINESS MANAGEMENT AND ACCOUNTING Jurnal Ilmu Komputer dan Sistem Informasi The Indonesian Journal of Computer Science Jurnal Testing dan Implementasi Sistem Informasi Jurnal Pustaka Robot Sister E-Amal: Jurnal Pengabdian Kepada Masyarakat
Desnelita, Yenny
Institut Bisnis Dan Teknologi Pelita Indonesia
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 Environmental Science Health Professions Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Social Sciences Other

Published : 32 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 32 Documents
Search

 1   2   3   4 
Technology-based Waste Management in Batik Production Purwati, Astri Ayu; Suriyanti, Linda Hetri; Retnawati, Sri Fitria; Yusrizal, Yusrizal; Desnelita, Yenny
International Conference on Business Management and Accounting Vol 3 No 1 (2024): Proceeding of International Conference on Business Management and Accounting (Nov
Publisher : Institut Bisnis dan Teknologi Pelita Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35145/icobima.v3i1.4525

Abstract

The environmental challenges faced by Batik Bujang and Batik Candafa, two Riau-based SMEs, stem primarily from the production process, which generates substantial wastewater containing harmful chemicals. The improper disposal of these by-products poses serious risks to water bodies and surrounding ecosystems. This research aims to design a waste management system using eco-friendly technologies capable of processing up to 40 liters of wastewater, thereby minimizing environmental harm. A mixed-method approach was adopted, combining quantitative analysis of waste volume and chemical composition with qualitative insights from SME owners. The findings indicate a significant reduction in chemical pollutants post-treatment, aligning with eco-sustainability goals. This study fills the research gap by providing a technological solution specifically tailored to the needs of small-scale batik producers, emphasizing sustainability and environmental responsibility.
Penerapan Algoritma K-Means Untuk Clustering Data Obat-Obatan Pada RSUD Pekanbaru Gustientiedina, Gustientiedina; Adiya, M. Hasmil; Desnelita, Yenny
Jurnal Nasional Teknologi dan Sistem Informasi Vol 5 No 1 (2019): April 2019
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v5i1.2019.17-24

Abstract

Perencanaan dari kebutuhan obat-obatan yang tepat dapat membuat pengadaan obat-obatan menjadi efektif dan efisien sehinggaobat-obatan dapat tersedia dengan cukup sesuai dengan kebutuhan serta dapat diperoleh pada saat yang diperlukan. Menganalisa pemakaian obat, perencanaan dan pengendalian obat-obatan dapat dilakukan pada data miningyaitu dengan clusterisasi.Metode yang akan di pakai untuk clustering data obat-obatan adalah algoritma K-Means yang mana merupakan metode clustering dengan non hirarki yang mempartisi data – data  kedalam cluster dimana data – datadengan karakteristik sama akan dikelompokkan padasatu cluster dan data – data dengan karakteristik yang berbeda akan dikelompokkan padacluster lainnya.Tujuan penelitian ini yaitumengelompokkan data obat-obatan pada rumah sakitsehingga dapat digunakan dalam acuan pengambilan keputusan perencanaan dan pengendaliaan persediaan obat-obatan di rumah sakit.
Decision Support System for Selecting Village Fund BLT Recipients using ROC and WASPAS Methods Manurung, Marisah Elfrida; Desnelita, Yenny; Hajjah, Alyauma; Duha, Yermias
Sistemasi: Jurnal Sistem Informasi Vol 14, No 4 (2025): 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.v14i4.4086

Abstract

The Village Fund Direct Cash Assistance (BLT) Program is a government initiative aimed at improving social welfare, reducing inequality, and supporting economically disadvantaged communities. However, in practice, the process of determining BLT recipients often faces issues of subjectivity and lack of transparency. This study aims to develop a Decision Support System (DSS) to assist village authorities in selecting BLT recipients objectively and accurately by utilizing the Rank Order Centroid (ROC) and Weighted Aggregated Sum Product Assessment (WASPAS) methods. The ROC method is used to assign weights to each criterion based on their level of importance, while the WASPAS method is applied to rank the recipient candidates according to the established weights. The DSS is developed as a web-based application to ensure easy access for village administrators. System testing results indicate that the ROC method consistently generates weights that reflect the prioritization of criteria, while the WASPAS method proves effective in producing final rankings of potential recipients. As a result, village leaders can make more objective and targeted decisions in determining BLT beneficiaries.
Penerapan Metode ELECTRE Untuk Menentukan Kualitas Pada Biji Kopi Arabika Marvin; Hajjah, Alyaumah; Yenny Desnelita
JEKIN - Jurnal Teknik Informatika Vol. 4 No. 3 (2024)
Publisher : Yayasan Rahmatan Fidunya Wal Akhirah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58794/jekin.v4i3.719

Abstract

Penelitian ini bertujuan untuk merangking kualitas salah satu jenis kopi yaitu kopi Arabika. Biji kopi arabika merupakan salah satu jenis utama kopi yang berkembang di Indonesia dengan menerapkan salah satu metode yang digunakan dalam Sistem Pendukung Keputusan (SPK) yaitu metode Elimination Et Choix Traduisant la Réalité (ELECTRE). Metode ELECTRE merupakan metode pengambilan keputusan untuk menyelesaikam masalah penentuan pilihan yang bersifat multi objective diantara beberapa kriteria, sehingga menghasilkan suatu analisa yang efektif dan efisien. Kriteria-kriteria input yang menjadi prioritas dalam penentuan kualitas kopi arabika yang relevan yaitu aroma, rasa, keasaman, body, keseragaman, keseimbangan, clean cup, rasa manis dan cupper point. Dengan menggunakan metode ini, pengevalusian kualitas kopi Arabika dapat dilakukan secara sistematis dan dapat menghasilkan informasi yang lebih akurat dan konsisten.
Analisis Sentimen Terhadap Ulasan Aplikasi IKD di Play Store Menggunakan Random Forest Kelvin H.; Erlin; Yenny Desnelita; Dwi Oktarina
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 14 No 3: Agustus 2025
Publisher : This journal is published by the Department of Electrical and Information Engineering, Faculty of Engineering, Universitas Gadjah Mada.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v14i3.20473

Abstract

The rapid growth of digital applications in population administration services has increased the importance of sentiment analysis to understand user perceptions more deeply. This study focuses on the Digital population identity (Identitas Kependudukan Digital, IKD), a digital identity application developed by the Indonesian government. It aims to classify user reviews of the IKD application into positive, neutral, and negative sentiments using the random forest algorithm. The dataset consisted of 28,134 user reviews from the Google Play Store, including usernames, review texts, timestamps, and star ratings. The research stages included data preprocessing, labeling, handling missing values, and text processing (cleansing, tokenizing, stopword removal, and stemming). The data were divided into 80% training and 20% testing sets. The best-performing model used the parameters: max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, and n_estimators=300, achieving an average accuracy of 83.78%. To address class imbalance, the synthetic minority oversampling technique (SMOTE) was applied, resulting in improved performance with an accuracy of 86.29%. Evaluation metrics before SMOTE showed 83.85% accuracy, 80.40% precision, 83.85% recall, and 81.73% F1 score. After SMOTE, precision increased to 81.22%, while accuracy and recall slightly decreased to 80.86%, with an F1 score of 81.03%. Furthermore, sentiment trend analysis using N-gram techniques (unigram, bigram, trigram) was conducted to identify frequently mentioned topics and user concerns. These insights support the research objective of guiding application improvements aligned with user needs and enhancing the overall digital service experience.
Perancangan Software Bimbingan dan Pengembangan Karir Siswa dalam Pengambilan Keputusan dan Konsultasi Irwan, Irwan; Gustientiedina, Gustientiedina; Sunarti, Sunarti; Desnelita, Yenny
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 4 No 4: Desember 2017
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (417.379 KB) | DOI: 10.25126/jtiik.201744464

Abstract

AbstrakTujuan dari penelitian ini mengemukakan pengembangan model sebuah perangkat lunak konseling siswa untuk melengkapi sebuah media sistem konsultasi bimbingan karir siswa. Perancangan software ini untuk mengkaji diri, mengenal diri sendiri, minat, bakat, kemampuan, pemilihan, penyesuaian sehingga siswa berupaya untuk mempersiapkan diri dengan meningkatkan kemampuan spiritual, pendidikan dan pelatihan, ketrampilan intelektual, ketrampilan berkomunikasi dan inter atau intra personal skill demi kehidupan di masa depan yang berupa alat penelusuran minat bakat berupa bimbingan dan pengembangan karir siswa. Penerapan metode certainty factor dapat merealisasikan jumlah kepercayaan dalam keputusan karir yang diambil dimana faktor kepastian dapat digunakan dengan berbagai kondisi. Dalam penelitian ini harus mengumpulkan nilai certainty factor keseluruhan kondisi yang ada. Penggunaan metode Certainty Factor (CF) dapat menunjukan tingkat kebenaran, keakuratan dari kemungkinan dalam pemilihan karir. Perancangan software bimbingan dan pengembangan karir dapat membantu konselor dalam  pemilihan karir yang diminati dengan terlebih dahulu menjawab pertanyaan pada user interface software.Kata kunci: Model Software, Certainty Factor,  Bimbingan, Pengembangan Karir, Pengambilan Keputusan AbstractThe purpose of this study suggests the development of a model student counseling software to complement a media student career guidance consulting system. The design of this software to assess themselves, know themselves, interests, talents, abilities, selection, adjustment so that students try to prepare themselves by improving spiritual skills, education and training, intellectual skills, communication skills and inter or intra personal skill for life in front in the form of talent interest search tools in the form of guidance and career development of students. The application of the certainty factor method can realize the amount of confidence in the career decision taken where the certainty factor can be used under various conditions. In this research must collect the value of certainty factor overall condition. The use of the Certainty Factor (CF) method can show the degree of truth, the accuracy of the possibilities in career selection. The design of software guidance and career development can help counselors in the selection of careers in interest by first answering questions on the user interface software.Keywords: Application Model, Conseling, Career Development, Decision Maker, Certainty Factor
Software Konsultasi Seleksi Karir Siswa menggunakan Metode Certainy Factor Irwan, Irwan; Gustientiedina, Gustientiedina; Hajjah, Alyauma; Desnelita, Yenny; Susanti, Wilda
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8 No 1: Februari 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.0811093

Abstract

Model software konsultasi seleksi karir sebagai alternatif  pengambilan keputusan dimana  konsultasi meliputi pemahaman karir, perencanaan karir, alternatif pilihan karir yang berupa alat penelusuran minat dan bakat siswa terhadap keputusan karir siswa menggunakan metode certainty factor berbasis sistem pakar. Penelitian ini menghasilkan model software konseling dalam memperoleh informasi penting tentang pengembangan karir siswa dan membantu memfasilitasi perkembangan individu siswa melalui bentuk layanan agar mampu merencanakan karirnya berdasarkan jurusan, minat, bakat, pengetahuan, kepribadian, kompetensi dan faktor-faktor lain yang mendukung kemajuan dirinya dalam menentukan pilihan dan keputusan yang sesuai dengan dunia kerja pilihan siswa.Sistem diuji menggunakanwhite box dan black boxdengan menunjukan hasil dimana sistem dapat digunakan sesuai kebutuhan. AbstractCareer selection consulting software model as an alternative decision making where consultation that includes career understanding,career understanding, career planning, alternative career choices in the form of a tool to trace students' interests and talents towards student career decisions using expert system based certainty factor method. This research produces a counseling software model in obtaining important information about student career development and helps facilitate individual students development  through a form of service in order to be able to plan his career based on majors, interests, talents, knowledge, personality, competencies and other factors that support his progress in determining choices and decisions that are appropriate to the world of work choice. The system is tested using white boxes and black boxes by showing the results where the system can be used as needed.
Penerapan Algoritma K-Means Untuk Clustering Data Obat-Obatan Pada RSUD Pekanbaru Gustientiedina Gustientiedina; M. Hasmil Adiya; Yenny Desnelita
Jurnal Nasional Teknologi dan Sistem Informasi Vol 5 No 1 (2019): April 2019
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v5i1.2019.17-24

Abstract

Perencanaan dari kebutuhan obat-obatan yang tepat dapat membuat pengadaan obat-obatan menjadi efektif dan efisien sehinggaobat-obatan dapat tersedia dengan cukup sesuai dengan kebutuhan serta dapat diperoleh pada saat yang diperlukan. Menganalisa pemakaian obat, perencanaan dan pengendalian obat-obatan dapat dilakukan pada data miningyaitu dengan clusterisasi.Metode yang akan di pakai untuk clustering data obat-obatan adalah algoritma K-Means yang mana merupakan metode clustering dengan non hirarki yang mempartisi data – data  kedalam cluster dimana data – datadengan karakteristik sama akan dikelompokkan padasatu cluster dan data – data dengan karakteristik yang berbeda akan dikelompokkan padacluster lainnya.Tujuan penelitian ini yaitumengelompokkan data obat-obatan pada rumah sakitsehingga dapat digunakan dalam acuan pengambilan keputusan perencanaan dan pengendaliaan persediaan obat-obatan di rumah sakit.
Dampak SMOTE terhadap Kinerja Random Forest Classifier berdasarkan Data Tidak seimbang Erlin Erlin; Yenny Desnelita; Nurliana Nasution; Laili Suryati; Fransiskus Zoromi
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 21 No. 3 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v21i3.1726

Abstract

Dalam aplikasi machine learning sangat umum ditemukan kumpulan data dalam berbagai tingkat ketidakseimbangan mulai dari ketidakseimbangan kecil, sedang sampai ekstrim. Sebagian besar model machine learning yang dilatih pada data tidak seimbang akan memiliki bias dengan memberikan tingkat akurasi yang tinggi pada kelas mayoritas dan sebaliknya rendah pada kelas minoritas. Tujuan penelitian ini adalah untuk mengevaluasi dampak dari SMOTE (Synthetic Minority Oversampling Technique) pada pengklasifikasi Random Forest untuk memprediksi penyakit jantung. Data berjumlah 299 berasal dari UCI Machine learning Repository digunakan untuk membangun model prediksi berdasarkan 12 variabel independen dan 1 variabel dependen. Kelas minoritas dalam dataset pelatihan di oversampling menggunakan teknik SMOTE (Synthetic Minority Oversampling Technique). Model dievaluasi tidak hanya menggunakan ukuran kinerja Accuracy dan Precision saja, namun juga menggunakan alternatif ukuran kinerja lainnya seperti Sensitivity, F1-score, Specificity, G-Mean dan Youdens Index yang lebih baik digunakan untuk data yang tidak seimbang. Hasil penelitian menunjukkan bahwa teknik SMOTE (Synthetic Minority Oversampling Technique) mampu mengurangi overfitting sekaligus meningkatkan kinerja model Random Forest pada semua indikator. Peningkatan skor Accuracy sebesar 3.45%, Precision 4.8%, Sensitivity 7.1%, F1-score 4.8%, Specificity 2.1%, G-Mean 4.4%, dan Youdens Index 6.3%. Penelitian ini membuktikan bahwa dalam menentukan pengklasifikasi dengan algoritma machine learning seperti Random Forest, kemiringan kelas dalam data perlu diperhitungkan dan diseimbangkan untuk hasil kinerja yang lebih baik.
Analisis Sentimen Terhadap Ulasan Aplikasi IKD di Play Store Menggunakan Random Forest Kelvin H.; Erlin; Yenny Desnelita; Dwi Oktarina
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 14 No 3: Agustus 2025
Publisher : This journal is published by the Department of Electrical and Information Engineering, Faculty of Engineering, Universitas Gadjah Mada.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v14i3.20473

Abstract

The rapid growth of digital applications in population administration services has increased the importance of sentiment analysis to understand user perceptions more deeply. This study focuses on the Digital population identity (Identitas Kependudukan Digital, IKD), a digital identity application developed by the Indonesian government. It aims to classify user reviews of the IKD application into positive, neutral, and negative sentiments using the random forest algorithm. The dataset consisted of 28,134 user reviews from the Google Play Store, including usernames, review texts, timestamps, and star ratings. The research stages included data preprocessing, labeling, handling missing values, and text processing (cleansing, tokenizing, stopword removal, and stemming). The data were divided into 80% training and 20% testing sets. The best-performing model used the parameters: max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, and n_estimators=300, achieving an average accuracy of 83.78%. To address class imbalance, the synthetic minority oversampling technique (SMOTE) was applied, resulting in improved performance with an accuracy of 86.29%. Evaluation metrics before SMOTE showed 83.85% accuracy, 80.40% precision, 83.85% recall, and 81.73% F1 score. After SMOTE, precision increased to 81.22%, while accuracy and recall slightly decreased to 80.86%, with an F1 score of 81.03%. Furthermore, sentiment trend analysis using N-gram techniques (unigram, bigram, trigram) was conducted to identify frequently mentioned topics and user concerns. These insights support the research objective of guiding application improvements aligned with user needs and enhancing the overall digital service experience.
Co-Authors Adiya, M. Hasmil Alfina Tiurmida Sitanggang Alyauma Hajjah Alyauma Hajjah Angriawan, Sherkhing Astri Ayu Purwati Debi Setiawan Dewi Nasien Dwi Oktarina Erlin Erlin Fadhillah, Maisya Fadrul Fadrul Fadrul, Fadrul Feri Candra Fransiskus Zoromi Gusrianty Gusrianty Gusrianty Gusrianty Gusrianty Gusrio Tendra Gustientiedina Gustientiedina Hajjah, Alyaumah Hamzah, Muhammad Luthfi Harry Kurniawan Harry Kurniawan Hendri Hendri Irwan Irwan Irwan Irwan Kelvin H. Laili Suryati Lioni Sianturi Lita Lita Lita, Lita Maisya Fadhillah Manurung, Marisah Elfrida Mario Cesar Marlim, Yulvia Nora Marvin Marwan, Deinike Wanita Mega Kartika Mery, Silvia Mimelientesa Irman Mohd. Elvisrafi Syawal Muhammad Luthfi Hamzah Muhammad Siddik Muhammad Siddik Noratama Putri, Ramalia Nurliana Nasution, Nurliana Purwati, Astri Ayu Rahman, Sarli Ramalia Nora Putri Ramalia Noratama Putri Ramalia Noratama Putri Rivaldo Famren Silaen Roni Sanjaya Rusilawati, Ermina Sarli Rahman Selvi Selvi Selvi Selvi Sianturi, Lioni Siddik, Muhammad Silvia Mery Sitompul, Silvia Sari Sri Fitria Retnawaty Sunarti Suriyanti, Linda Hetri Suyan Suyan Syawal, Mohd. Elvisrafi Wahyu Joni Kurniawan, Wahyu Joni Wilda Susanti Yermias Duha Yusrizal Zulfikri Zulyasdi, Ivan Afriyan