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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Robotics and Automation (IJRA) IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Scientific Journal of Informatics Proceeding of the Electrical Engineering Computer Science and Informatics Sistemasi: Jurnal Sistem Informasi Jurnal Teknologi dan Sistem Komputer Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer SISFOTENIKA IJCIT (Indonesian Journal on Computer and Information Technology) Knowledge Engineering and Data Science IT JOURNAL RESEARCH AND DEVELOPMENT JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL REKAYASA TEKNOLOGI INFORMASI SINTECH (Science and Information Technology) Journal JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Sebatik ILKOM Jurnal Ilmiah Digital Zone: Jurnal Teknologi Informasi dan Komunikasi MIND (Multimedia Artificial Intelligent Networking Database) Journal Prosiding SAKTI (Seminar Ilmu Komputer dan Teknologi Informasi) EXPLORE TELKA - Telekomunikasi, Elektronika, Komputasi dan Kontrol Jurnal SITECH : Sistem Informasi dan Teknologi Building of Informatics, Technology and Science JISKa (Jurnal Informatika Sunan Kalijaga) Sains, Aplikasi, Komputasi dan Teknologi Informasi Journal of Innovation Information Technology and Application (JINITA) Journal of Information Technology and Its Utilization Innovation in Research of Informatics (INNOVATICS) Jurnal Pengabdian UNDIKMA Jurnal Teknik Informatika (JUTIF) Vivabio : Jurnal Pengabdian Multidisiplin Jurnal PTI (Jurnal Pendidikan Teknologi Informasi) JP2KG AUD (Jurnal Pendidikan, Pengasuhan, Kesehatan dan Gizi Anak Usia Dini) JUSTIN (Jurnal Sistem dan Teknologi Informasi) BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer JAIA - Journal of Artificial Intelligence and Applications EXPLORE Jurnal Bina Komputer JAPI: Jurnal Akses Pengabdian Indonesia Data Sciences Indonesia (DSI) Journal Of Artificial Intelligence And Software Engineering The Indonesian Journal of Computer Science Inovasi Teknologi Masyarakat Jurnal Pengabdian Siliwangi
Puspitasari, Novianti
Program Studi Informatika, Fakultas Teknik, Universitas Mulawarman
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Pengaplikasian Google Docs Dan Google Slides Dalam Membantu Mengerjakan Tugas Siswa Di SMPN 44 Samarinda Septiarini, Anindita; Puspitasari, Novianti; Irfan, Aliya; Wintin, Chintia Liu; Wibisono, Bramantyo Ardi Harimurti; Maharani, Agustina Dwi; Fuad, Natalie; Laraswati, Sherina; Gunawan, Ayu Lestari
Inovasi Teknologi Masyarakat (INTEKMAS) Vol. 1 No. 1 (2023): June 2023
Publisher : Wadah Inovasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53622/intekmas.v1i1.192

Abstract

Kegiatan pelayanan masyarakat ini bertujuan untuk memberikan pengenalan dan pemahaman mengenai penggunaan Google Docs dan Google Slides kepada siswa-siswa SMPN 44 Palaran Samarinda. Hal ini dilakukan untuk mendukung pemahaman siswa terhadap materi yang diajarkan di SMPN 44 Palaran Samarinda. Aplikasi Google Docs dan Google Slides sangat cocok bagi siswa yang ingin bekerja sama secara langsung dalam satu berkas. Dalam praktiknya, pengetahuan dan penggunaan aplikasi Google Docs dan Google Slides di SMPN 44 Palaran Samarinda masih kurang. Oleh karena itu, melalui kegiatan ini dilakukan sosialisasi dan pelatihan penggunaan Google Docs dan Google Slides untuk memberikan pengetahuan kepada siswa bahwa bekerja sama dalam satu berkas menggunakan Google Docs atau Google Slides tidaklah sesulit yang dibayangkan, serta memberikan edukasi tentang pentingnya bekerja sama dalam satu berkas. Selain itu, kegiatan ini juga bertujuan untuk memberikan informasi mengenai manfaat Google Docs dan Google Slides bagi pemula.
Classification for Determining the Level of Drugs Dependence Using the Naïve Bayes Classifier Puspitasari, Novianti; Ajay, Muhammad; Wati, Masna; Septiarini, Anindita
IT Journal Research and Development Vol. 9 No. 1 (2024)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2024.16319

Abstract

Drug users or abusers are people who use narcotics or psychotropic drugs without supervision or medical indication from a doctor. Before undergoing rehabilitation, drug users must first undergo an examination to determine their level of drug dependence so that they can receive medical treatment according to their level of drug dependence. Determining the level of drug dependence requires a technique that can provide labels or categories of data for drug users based on the user's condition or influential criteria. This study applies the Naïve Bayes Classifier method to a system to determine the level of drug dependence. This study uses medical record data from 220 drug users. The user's medical record data is processed using data mining stages consisting of data selection, data cleaning, data transformation, and division of training and test data to produce 120 training data and 100 test data. The results of the Naive Bayes Classifier method calculation resulted in 29 users having a trial level of dependence (mild), 42 identified as having a regular level of dependence (moderate), and 29 others as users with a severe level of dependence. The confusion matrix testing was very accurate, namely, 94% accuracy, 95% precision value, and 92% recall. Meanwhile, the system that has been built can run very well. Based on the results of the research that has been conducted, this research can contribute to determining the level of dependence of drug addicts objectively so that related parties can provide rehabilitation or appropriate treatment to drug addicts.
PERAN TRI PUSAT PENDIDIKAN DALAM PENDIDIKAN SEKSUAL ANAK USIA DINI Gerda, Misselina Madya; Puspitasari, Novianti; Septiani, Reni D.; Dewi, Nurul Kusuma
JP2KG AUD (Jurnal Pendidikan, Pengasuhan, Kesehatan dan Gizi Anak Usia Dini) Vol. 2 No. 2 (2021)
Publisher : PG PAUD Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jp2kgaud.2021.2.2.97-106

Abstract

Prediction of Budget Planning Using the Long Short Term Memory Ambari, Nasser; Puspitasari, Novianti; Septiarini, Anindita
Journal of Artificial Intelligence and Software Engineering Vol 5, No 1 (2025): March
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i1.6428

Abstract

Keputusan merupakan elemen kunci dalam manajemen perusahaan, karena perencanaan yang baik menjadi faktor penentu kesuksesannya. Salah satu aspek penting dalam perencanaan adalah prediksi penjualan. Sebuah perusahaan properti dapat mengalami kesulitan aliran kas akibat over budget, sehingga memaksa perusahaan untuk meninjau kembali strategi pemasaran. Penelitian ini menggunakan Long Short Term Memory (LSTM) untuk membantu perusahaan dalam mengurangi risiko over budget di masa depan. Metode Long Short Term Memory (LSTM) mampu menghasilkan model prediksi dengan akurasi tinggi. Data penelitian berupa data pendapatan penjualan properti dari sebanyak 107 data. Hasil pengujian menunjukkan bahwa penggunaan LSTM dengan perbandingan data latih dan data uji sebesar 90:10, 200 epoch, dan learning rate sebesar 0.005 menghasilkan nilai Root Mean Square Error (RMSE) terendah sebesar 0.128883554. Hasil prediksi menunjukkan pendapatan penjualan yang terus menurun selama tiga tahun.
Perbandingan Metode KNN dan Naive Bayes untuk Klasifikasi Kelulusan Mahasiswa Pada Mata Kuliah Probstat Yuyun Nabilawati Rumbia; Raihanfitri Adi Kalipaksi; Alvito Gabbriel Saputra; Muhammad Dzacky; Alif Rifa’i; Septiarini, Anindita; Puspitasari, Novianti
JURNAL PTI (PENDIDIKAN DAN TEKNOLOGI INFORMASI) FAKULTAS KEGURUAN ILMU PENDIDIKAN UNIVERSITA PUTRA INDONESIA "YPTK" PADANG Vol. 12 (2025) No.1
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/jpti.v12i1.228

Abstract

Penelitian ini bertujuan untuk membandingkan kinerja dua algoritma klasifikasi data mining, yaitu K-Nearest Neighbor (KNN) dan Naive Bayes, dalam mengklasifikasikan kelulusan mahasiswa pada mata kuliah Probabilitas dan Statistika angkatan 2022 Program Studi Informatika Universitas Mulawarman. Dataset yang digunakan merupakan data asli yang diberikan oleh dosen pengampu probas, terdiri atas 136 entri yang dibagi dengan rasio 70:30 menggunakan library Scikit-learn. Penelitian ini secara khusus menguji performa klasifikasi pada data numerik mentah tanpa melalui proses normalisasi. Metode KNN dan Naive Bayes dievaluasi menggunakan metrik akurasi, presisi, recall, dan F1-score untuk mengukur tingkat keakuratan dalam memprediksi kelulusan mahasiswa. Hasil evaluasi menunjukkan bahwa KNN memiliki performa yang lebih unggul dibandingkan Naive Bayes dalam seluruh metrik pengujian. KNN memperoleh akurasi sebesar 94,87%, sementara Naive Bayes hanya mencapai 87,80%, sehingga dapat disimpulkan bahwa KNN lebih efektif dalam menangani klasifikasi pada data numerik yang tidak dinormalisasi.
Pemilihan Asuhan Nutrisi Untuk Menjaga Kadar Kolesterol Menggunakan Metode Weighted Aggregated Sum Product Assessment (WASPAS) Syachmiral, Zidane Althaariq; Puspitasari, Novianti; Taruk, Medi
Jurnal Rekayasa Teknologi Informasi (JURTI) Vol 9, No 1 (2025): Jurnal Rekayasa Teknologi Informasi (JURTI)
Publisher : Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jurti.v9i1.20551

Abstract

Di Indonesia sebagian besar masyarakat memiliki angka kadar kolesterol melebih batas normal. Pola makan yang tidak seimbang dan gaya hidup yang tidak sehat menjadi penyebab terbesar masyarakat mengalami peningkatan kadar kolesterol. Pengaturan pola makan yang memperhatikan zat gizi yang terkandung di dalam suatu makanan dapat menjadi solusi untuk mengurangi kadar kolesterol dalam tubuh. Namun, pemilihan asupan nutrisi yang sesuai dengan kebutuhan tubuh masih susah dilakukan oleh masyarakat. Sistem pendukung keputusan pemilihan asupan nutrisi yang optimal untuk menjaga kadar kolesterol menggunakan metode Weighted Aggregated Sum Product Assessment (WASPAS) menjadi solusi untuk mengatasi permasalahan tersebut. Metode WASPAS dipilih karena kemampuannya dalam mengintegrasikan keunggulan metode Weighted Sum Model (WSM) dan Weighted Product Model (WPM), sehingga mampu memberikan penilaian yang lebih akurat dan fleksibel terhadap alternatif nutrisi berdasarkan berbagai kriteria. Kriteria yang digunakan meliputi kandungan lemak jenuh, lemak tak jenuh, asam lemak omega-3, serat, dan vitamin C. Data diperoleh dari sumber literatur gizi terpercaya berupa jenis asupan nutrisi sebanyak 25 jenis untuk menghasilkan peringkat alternatif nutrisi terbaik. Hasil penelitian menunjukkan bahwa metode WASPAS mampu memberikan pilihan asupan makanan yang mendukung pengendalian kadar kolesterol.
Comparison of FMADM TOPSIS and FMADM WP in Determining Recipients of the Family Hope Program (PKH) Assistance Puspitasari, Novianti; Kurniati, Wendy; Hatta, Heliza Rahmania
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9852

Abstract

Fuzzy Multi-Attribute Decision Making (FMADM) TOPSIS and WP methods are frequently employed to identify potential recipients of government assistance. The Family Hope Program (PKH) is a government social assistance program designed to improve the welfare of underprivileged individuals. However, the process of distributing this assistance often faces obstacles in the form of inaccuracy in determining recipients. This study compares FMADM TOPSIS and WP to evaluate their effectiveness in objectively determining potential PKH recipients. The criteria for potential PKH recipients are eleven criteria obtained from the social service based on government regulations and PKH assistants. Meanwhile, the alternatives for this study are fifty samples of family data for potential PKH recipients. This study employs a sensitivity test method to assess the accuracy of the results obtained from each method. The results of the study show that FMADM TOPSIS produces a higher level of accuracy of 94% compared to FMADM WP. This study is expected to be able to contribute to choosing the right decision-making method to determine potential recipients of social assistance.
Clustering Status Keberlanjutan Karyawan Kontrak Menggunakan Algoritma K-Medoids Ramadhaniaty, Dinda; Puspitasari, Novianti; Septiarini, Anindita
TELKA - Telekomunikasi Elektronika Komputasi dan Kontrol Vol 10, No 2 (2024): TELKA
Publisher : Jurusan Teknik Elektro UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/telka.v10n2.97-108

Abstract

Karyawan kontrak adalah karyawan tidak tetap yang dipekerjakan untuk melakukan pekerjaan didalam perusahaan, dan tidak ada jaminan kelangsungan masa kerjanya. Penilaian terhadap karyawan tetap maupun karyawan kontrak dilakukan dengan cara menilai kinerja seorang karyawan tersebut. Oleh karena itu, perusahaan selalu melakukan evaluasi terhadap kinerja karyawannya. Penelitian ini bertujuan untuk mengelompokkan status karyawan kontrak yang layak diperpanjang atau tidak layak secara objektif sehingga mempermudah perusahaan dalam menganalisa data agar hasil lebih tepat dan dapat menghindari kecurangan dalam prosesnya. Algoritma yang digunakan dalam penelitian ini adalah K-Medoids menggunakan tiga metode pengukuran jarak yaitu Euclidean Distance, Manhattan Distance dan Chebyshev Distance. Sementara, untuk uji akurasi menggunakan Silhouette Coefficient (SC) dan Sum of Square Error (SSE). Data yang digunakan adalah data penilaian karyawan kontrak berjumlah 42 karyawan kontrak. Berdasarkan pengujian nilai SC diperoleh bahwa dari ketiga metode pengukuran jarak yang digunakan, chebyshev distance menghasilkan nilai yang mendekati 1 dengan nilai 0.3220214. Sedangkan, hasil uji cluster SSE diperoleh bahwa nilai error terkecil dimiliki oleh 2 cluster. Lebih lanjut, hasil pengelompokan status keberlanjutan karyawan kontrak menempatkan 20 data ke dalam kategori Tidak Layak (C1) dan 22 data ke dalam kategori Layak (C2). Contract employees are temporary workers hired to perform tasks within a company without any guarantee of tenure. Evaluations of both permanent and contract employees are conducted based on their performance, which is essential for the company's continuous performance assessment. This study aims to classify the status of contract employees to determine their eligibility for contract extension objectively. This objective classification facilitates the company's data analysis, resulting in more accurate outcomes and minimizing the risk of fraud in the evaluation process. The algorithm employed in this research is K-medoids, utilizing three distance measurement methods: Euclidean distance, Manhattan distance, and Chebyshev distance. The accuracy of the classifications was tested using the silhouette coefficient (SC) and sum of squares error (SSE). The data set comprised performance assessments of 42 contract employees. Testing the SC values revealed that among the three distance measurement methods, the Chebyshev distance yielded the closest value to 1, specifically 0.3220214. Additionally, the SSE cluster test results indicated that the smallest error value was achieved with 2 clusters. Consequently, the grouping results classified 20 employees as Ineligible (C1) and 22 employees as Eligible (C2) for contract extension.
A comparative study of machine learning methods for drug type classification Tejawati, Andi; Suprihanto, Didit; Ery Burhandenny, Aji; Saipul, Saipul; Puspitasari, Novianti; Septiarini, Anindita
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9477

Abstract

Drugs, commonly called narcotics, are dangerous substances that, if consumed excessively, can result in addiction and even death. Drug abuse in Indonesia has reached a concerning stage. In 2017, the National Narcotics Agency detected 46,537 drug-related incidents, including methamphetamine, marijuana, and ecstasy. There are 4 types of substances that can affect drug users, such as hallucinogens, depressants, opioids, and stimulants. A machine learning approach can detect these substances using user symptom data as input. This study uses six different methods in classifying, including decision tree, C.45, K-nearest neighbor (KNN), random forest, and support vector machine (SVM). The dataset comprises 144 data and 21 attributes based on the user's symptoms. The evaluation method in this study uses cross-validation with K-fold values of 5 and 10 and uses three parameters: precision, recall, and accuracy. KNN yields the most optimal results by using K=1 and K-fold 10 in the Euclidean and Minkowski types. The model achieves precision, recall, and accuracy of 91.9%, 91.7%, and 91.67%, respectively.
Enhancing crude palm oil quality detection using machine learning techniques Puspitasari, Novianti; Hairah, Ummul; Kamila, Vina Zahrotun; Hamdani, Hamdani; Septiarini, Anindita; Masa, Amin Padmo Azam
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp2955-2963

Abstract

Indonesia, a leading nation in the palm oil industry, experienced a significant increase of 15.62% in crude palm oil (CPO) exports in 2020, effectively meeting the global need for vegetable oil and fat. Therefore, the subjective assessment of CPO quality, influenced by differences in human evaluations, may lead to inconsistencies, necessitating the adoption of machine learning methods. There are several categories of CPO, such as bad and excellent. Machine learning can determine the quality of CPO itself. This study utilizes two distinct categories to measure the quality of CPO. CPO quality data is collected and processed into pre-processing data, in classifying using several methods such as artificial neural network (ANN), k-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), naïve Bayes (NB), and C.45 using the cross-validation evaluation parameter. The best results are obtained by C.45 and DT with an accuracy of 99.98%.
Co-Authors Abu Bakar Adelowys Sinaga Adhistya Erna Permanasari Adnan, Fahrizal Afifah, Dinda Nur AHMAD ANSYORI Ahmad Suryadi, Ahmad Ahmad Wahbi Fadillah Ajay, Muhammad Aji Ayu Muvita Putri Alameka, Faza Alameka, Faza Alfajriani Alfajriani Alfredo Sinaga Ali Sholihin Alif Rifa’i Almasari Aksenta Alvito Gabbriel Saputra Ambari, Nasser Andre Ardin Maulana Anindita Septiarini, Anindita Anton Prafanto Arinda Mulawardani Kustiawan Asdar Zulkiawan Awang Harsa Kridalaksana Brins Leonard Pailan Budiman, Edy Didit Suprihanto, Didit Eka Priyatna, Surya Ery Burhandenny, Aji Fahrul Agus Fairil Anwar Fajar Fatimah Farisha Rizky Amalia Fathia Nuq Qamarina Fauzan, Ahmad Nur Fayza Virdana Addiza Faza Alameka Faza Alameka Fazma Urmila Jannah Helmi Puadi Firdaus, Muhammad Firdaus, Muhammad Bambang Fornia, Daviana Dwitasari Enka Frans Karta Sayoga Sitohang Fuad, Natalie Gerda, Misselina Madya Gubtha Mahendra Putra Gunawan, Ayu Lestari Gunawan, Santika Haeruddin, Haeruddin Hairah, Ummul Hairah, Ummul Hakim, Muhammad Irvan Hamdani Hamdani Hamdani Hamdani Hanif, Ahmad Luthfi Hanung Adi Nugroho Hatta, Heliza Rahmania Haviluddin Haviluddin Haviuddin, Haviluddin Heliza Hatta Heliza Rahmania Hatta, Heliza Rahmania Helmi Puadi, Fazma Urmila Jannah Hemelia, Junita Henderi . Heni Sulastri Hidayat, Ahmad Nur Hijratul Aini Iin Nurkarima Indah Wulan Lestari Irfan, Aliya Islamiyah Islamiyah Joan Angelina Widians, Joan Angelina Julius Rinaldi Simanungkalit Kalista, Nazwa Nur Maulida Qintani Kamila, Vina Zahrotun Kurniati, Wendy Kurniawan, Tri Basuki Lalu Delsi Samsumar, M.Eng. Laraswati, Sherina Latifa Gorriana Gusmaningrum Lempas, Gidion M. Rizky Nilzamyahya Maharani, Agustina Dwi Mahendra, Dicky Alvian Masa, Amin Padmo Azam Masna Wati Mega Yoalifa Mewengkang, Alfrina Muhammad Abdillah Muhammad Dzacky Muhammad Firdaus Mulia, Amalia Budiana Nataniel Dengen Noval Bayu Setiawan Nur Hasanah Nurhidayat, Rifki Nurkarima, Iin Nursari, Ayla Nurul Kusuma Dewi, Nurul Kusuma Olivia Octavia Pakpahan, Herman Santoso Paripurna, Rian Bintang Pasorong, Hillary Bella Patricia Chandra Pebianoor, Pebianoor Prafanto, Anton Pramudya, Pranata Eka Pratama, Fhanji Wilis Pusparini, Faradilla Rahayu, Ervina Rahayu, Rizqi Widya Rahmat Kamara Raihanfitri Adi Kalipaksi Ramadhaniaty, Dinda Rayner Alfred Reza Nur Muhammad Rezky, Muhammad Rima Yustika Hasnida Rita Diana Rizky Pratama Putra Rondongalo Rismawati Rosita, Aliffia Rosmasari Rosmasari Rosmasari Rosmasari Rosmasari Rosmasari, Rosmasari Rosmasari, Rosmasari Saipul, Saipul Sandyanegara, Aryasena Bela Sarira, Brayen Tisra Septiani, Reni D. Septirini, Anindita Setyadi, Hario Jati Sihombing, Yobel Fernanda Simanungkalit, Julius Rinaldi Stefanie Stefanie Sugandi Sugandi Sulastri, Heni Sumaini Sumaini Suryani Junita Patandianan Syachmiral, Zidane Althaariq Syarah, May Siti Taruk, Medi Tejawati, Andi Tjikoa, Ade Fiqri Vicky Pranandika Wijaksana Wahyudi, Moh Ikhwan Waksito, Alan Zulfikar Wati, Masna Wibisono, Bramantyo Ardi Harimurti Widians, Joan Angelina Wijaya, Zhienka Putri Willyardo Tampubolon Wintin, Chintia Liu Yasmin, Annisa Yuyun Nabilawati Rumbia zahra salsabila Zainal Arifin Zali, Wahyu Noor