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All Journal Jurnal Informatika dCartesian: Jurnal Matematika dan Aplikasi Jurnal Simetris Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Informatika dan Teknik Elektro Terapan SMATIKA Sistemasi: Jurnal Sistem Informasi Jurnal Informatika IJCIT (Indonesian Journal on Computer and Information Technology) Jurnal EMT KITA JIKO (Jurnal Informatika dan Komputer) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Pilar Nusa Mandiri Syntax Literate: Jurnal Ilmiah Indonesia JUTEI (Jurnal Terapan Teknologi Informasi) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi INTECOMS: Journal of Information Technology and Computer Science MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal ULTIMA Computing JURIKOM (Jurnal Riset Komputer) Jurnal Informatika JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) ComTech: Computer, Mathematics and Engineering Applications Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Progresif: Jurnal Ilmiah Komputer Zonasi: Jurnal Sistem Informasi Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Tekinkom (Teknik Informasi dan Komputer) Abdimasku : Jurnal Pengabdian Masyarakat INFOKUM Aiti: Jurnal Teknologi Informasi Jurnal Teknologi Informatika dan Komputer IJECS: Indonesian Journal of Empowerment and Community Services J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Ekonomi JUSTIN (Jurnal Sistem dan Teknologi Informasi) International Journal Software Engineering and Computer Science (IJSECS) Jurnal Media Computer Science Malcom: Indonesian Journal of Machine Learning and Computer Science STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer IT-Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi J-Icon : Jurnal Komputer dan Informatika Jurnal Pendidikan Teknologi Informasi (JUKANTI) Jurnal Indonesia : Manajemen Informatika dan Komunikasi Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) d'Cartesian: Jurnal Matematika dan Aplikasi JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Magdalena Ariance Ineke Pakereng
Universitas Kristen Satya Wacana
Religion Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Public Health Social Sciences Other

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IDENTIFIKASI PENGARUH KUALITAS UDARA TERHADAP KONDISI PASIEN COVID-19 DENGAN 1 ALGORITMA NAIVE BAYES Febriyanti, Monica Dias; Wowor, Alz Danny; Pakereng, Magdalena A. Ineke
JURNAL INFORMATIKA DAN KOMPUTER Vol 8, No 2 (2024): September 2024
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiko.v8i2.867

Abstract

Pandemi Covid-19 atau corona virus sangat meresahkan bagi masyarakat Indonesia karena penyebarannyayang sangat mudah, virus ini dapat ditularkan melalui tetesan kecil (droplet) dari mulut maupun hidung pen-derita covid-19. Pada kasus tertentu virus ini juga bisa menginfeksi pernafasan berat sehingga menyebabkanpneumonia (infeksi paru-paru). Masyarakat yang memiliki daya tahan tubuh lemah sangat mudah tertular virusini, salah satu penyebab daya tahan tubuh melemah yaitu kualitas udara yang tidak bagus oleh karena itu pene-litian ini melakukan identifikasi pengaruh dari kualitas udara terhadap kondisi pasien covid-19 menggunakanalgoritma Naïve Bayes. Data diperoleh melalui website corona.jakarta.go.id dan Jakarta OpenData, setelah prep-rocessing diperoleh 610 data yang dibagi menjadi 80% data training dan 20% data testing. Penggunaan denganAlgoritma Naïve Bayes menunjukkan nilai akurasi yang tinggi yaitu 82,73%. Hasil identifikasi berdasarkan al-goritma naïve bayes dalam empat class, class sangat berpengaruh 2,3%, class berpengaruh 62,8%, class kurangberpengaruh 34,5%, dan class tidak berpengaruh 0.4%. Dengan demikian kualitas udara ikut mempengaruhikondisi pasien covid-19 di wilayah Propinsi DKI Jakarta.
Analisis Sentimen Komentar Youtube Terhadap Wawancara Presiden Prabowo Menggunakan Machine Learning Dan Orange Data Mining E.V. Sihombing, Kristina; A. Ineke Pakereng, Magdalena
STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer Vol. 4 No. 4 (2025): November
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/storage.v4i4.6478

Abstract

Media sosial seperti Youtube telah menjadi platform utama dalam menyampaikan opini publik, termasuk dalam isu-isu politik nasional. Penelitian ini bertujuan untuk menganalisis sentimen komentar publik terhadap video wawancara Presiden Prabowo Subianto yang ditayangkan di kanal Youtube Najwa Shihab. Komentar yang dikumpulkan melalui Youtube API diproses menggunakan tahapan text preprocessing dan pelabelan semi-supervised dengan Orange Data Mining. Tiga algoritma machine learning Naive bayes, Random Forest, dan Support Vector Machine (SVM) digunakan untuk melatih model klasifikasi sentimen. Hasil evaluasi menunjukkan bahwa SVM menghasilkan kinerja terbaik dengan akurasi sebesar 74% dan f1-score tertimbang 0.73. Random Forest mencapai akurasi 73%, sedangkan Naïve Bayes 66%. Temuan ini menunjukkan bahwa model klasik memiliki keterbatasan dalam memahami konteks dan sentimen tersirat, khususnya pada komentar berbahasa tidak formal. Oleh karena itu, penelitian selanjutnya disarankan menggunakan pendekatan deep learning seperti BERT dan metode pelabelan manual untuk meningkatkan kualitas klasifikasi.
Square Transposition Method with Adaptive Key Flexibility and Strong Diffusion Performance Magdalena Ariance Ineke Pakereng; Alz Danny Wowor; Yos Richard Beeh; Felix David; Erwien Christianto; Vincent Exelcio Susanto; Claudio Canavaro
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 3 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i3.5004

Abstract

The Square Transposition method has notable potential in enhancing diffusion within block encryption systems; however, its application is typically limited to perfect square key lengths. The objective of this study is to reconstruct the method to accommodate non-square key lengths by utilizing two square matrices. To assess the effectiveness of the proposed approach, the method of this study uses a comparative analysis conducted against the transposition structures found in DES and AES algorithms, both of which are cryptographic standards established by NIST. The comparison is strictly limited to the transposition component, excluding other components of the full encryption framework. The evaluation involves Monobit, Block Bit, and Run Tests, along with Pearson correlation analysis between plaintext and ciphertext. Tests are conducted on 16 input variations across three key sizes: 128-bit, 256-bit, and 512-bit. The results of this study show that the proposed method achieves lower correlation values (r = 0.02) compared to DES (r = 0.07) and AES (r = 0.05). The conclusion of this study is that these findings indicate the approach offers improved key flexibility and diffusion capability, making it a promising transposition component for block cipher encryption systems. This reconstruction contributes a novel transposition structure that is compatible with non-square key sizes, thereby enhancing both diffusion strength and adaptability in modern cryptographic applications.
ANALISIS KEAMANAN JARINGAN WIFI DAN CCTV MENGGUNAKAN ZENMAP SEBAGAI NETWORK VULNERABILITY SCANNER Helena Dorthea Fiay; Magdalena A. Ineke Pakereng M
INTECOMS: Journal of Information Technology and Computer Science Vol. 9 No. 2 (2026): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/dr1nrv25

Abstract

 Penelitian ini membahas analisis keamanan jaringan WiFi dan sistem CCTV berbasis IP dengan memanfaatkan Zenmap sebagai antarmuka grafis dari Nmap. Latar belakang penelitian berangkat dari meningkatnya penggunaan perangkat Internet of Things (IoT) yang sering kali memiliki konfigurasi keamanan lemah, seperti penggunaan kata sandi bawaan, enkripsi rendah, serta firmware yang tidak diperbarui. Melalui metode vulnerability assessment, penelitian ini melakukan pemindaian jaringan untuk mengidentifikasi host aktif, port terbuka, layanan yang berjalan, serta potensi kerentanan pada perangkat WiFi dan CCTV di Studio Pemrograman S-Code Salatiga. Hasil pemindaian menunjukkan adanya port berisiko yang terbuka serta layanan yang rentan terhadap eksploitasi. Analisis dilakukan secara deskriptif- kualitatif dengan menilai tingkat risiko dan memberikan rekomendasi mitigasi, seperti pembaruan firmware, penggantian kredensial default, penerapan WPA3, serta segmentasi jaringan. Penelitian ini diharapkan dapat menjadi acuan praktis bagi administrator jaringan maupun organisasi lain dalam meningkatkan keamanan infrastruktur berbasis IoT. Kata Kunci: Keamanan Jaringan, WiFi, CCTV/IP Camera, Zenmap / Nmap, Vulnerability Assessment
ANALISIS KEPUASAN MAHASISWA DALAM PEMANFAATAN CHATGPT SEBAGAI PENDUKUNG PEMBELAJARAN DENGAN PENDEKATAN DATA MINING Lusman, Chrizanny Winifred; Pakereng, Magdalena A. Ineke
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 4 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i4.7334

Abstract

Perkembangan zaman membawa kita kedalam segenap fenomena yang terjadi dalam lingkup masyarakat, khususnya dalam kehidupan mahasiswa yang mulai mengenal Artificial Intelegence yaitu ChatGPT salah satunya. Sehingga dalam pelaksanaannya penelitian ini dilakukan berdasarkan kepuasan yang dimiliki mahasiswa terhadap aplikasi ChatGPT serta. Dalam penelitian ini juga mengukur bagaimana pemahaman mahasiswa terhadap ChatGPT yang kemudian orientasinya kedalam frekuensi penggunaan serta pengaruh apa yang diperoleh mahasiswa. Kepuasan pada aplikasi ChatGPT yang diukur dengan menggunakan metode Data Mining. Dalam menjawab fenomena tersebut maka terdapat tujuan pada penelitian ini yaitu antara lain untuk mengetahui apakah mahasiswa memiliki kepuasan yang signifikan terhadap aplikasi ChatGPT. Hasil penelitian menunjukkan bahwa seluruh variabel memiliki tingkat kepuasan yang tinggi. Dalam mengukur tingkat pemahaman mahasiswa terhadap ChatGPT diperoleh nilai akurasi yaitu diantaranya 84,6%, nilai frekuensi penggunaan 77,4%, nilai kepuasan 74,8%, dan nilai pengaruh 76,2%. Hasil ini menunjukkan bahwa penggunaan ChatGPT sebagai alat bantu belajar memberikan tingkat kepuasan yang relative serupa bagi mahasiswa.
ANALISIS SENTIMEN MAHASISWA TERHADAP SIASAT UKSW BERDASARKAN KUESIONER MENGGUNAKAN METODE LOGISTIC REGRESSION Robby Adrian Fajar Sulistya Sulistya; Magdalena Ariance Ineke Pakereng
Jurnal Pendidikan Teknologi Informasi (JUKANTI) Vol 9 No 1 (2026): JURNAL PENDIDIKAN TEKNOLOGI INFORMASI (JUKANTI) EDISI APRIL 2026
Publisher : Universitas Citra Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37792/jukanti.v9i1.1935

Abstract

SIASAT is the primary academic information system at Satya Wacana Christian University (UKSW) used by students for academic administrative activities. This study aims to analyze students’ sentiment toward SIASAT using a Logistic Regression classifier based on open-ended responses collected through an online questionnaire. A total of 58 textual responses were manually labeled into two classes, namely positive and negative. The research stages include text preprocessing (lowercasing, cleaning, tokenization, stopword removal, and stemming), feature weighting using TF-IDF, handling class imbalance through Random Oversampling, and classification using Logistic Regression. Evaluation was performed using Stratified 5-Fold Cross-Validation, with oversampling applied exclusively to the training data within each fold to prevent data leakage. The model achieved an average accuracy of 70% and a weighted F1-score of 70%. These results suggest that Logistic Regression provides a promising baseline performance for Indonesian text sentiment classification on a small-scale dataset, although its generalizability remains limited due to the small sample size and the manual labeling process.
Pembekalan Design Thinking Untuk Pembentukan Karakter Wirausaha Muda Solutif-Inovatif Pada Murid SMA/K di Kota Palu Dwi Hosanna Bangkalang; Nina Setiyawati; Evangs Mailoa; Deasy Carolina; Magdalena Ariance Ineke Pakereng; Willson Mangoki
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 9, No 1 (2026): JANUARI 2026
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v9i1.3187

Abstract

Salah satu tantangan pada wirausaha muda adalah adanya persoalan pada tahap analisis pasar sebelum usaha mulai dirintis yakni produk yang dihasilkan tidak sesuai dengan kebutuhan pasar. Selain itu, minat siswa untuk berwirausaha masih cenderung rendah. Hal tersebut dipengaruhi oleh pola pikir yang kurang adaptif terhadap risiko dan ketidakpastian, serta ketakutan terhadap kegagalan. Kegiatan Pengabdian kepada Masyarakat (PkM) ini dilakukan di SMKN 1 Palu dan SMAN 2 Palu dengan memberikan workshop Design Thinking kepada siswa untuk membentuk karakter para siswa menjadi gigih, empati, peka, dan kreatif. Dimana karakter ini dibutuhkan untuk menjadi seorang wirausaha. Workshop dilakukan dengan metode praktik untuk meningkatkan partisipasi setiap peserta. Pendekatan berbasis praktik dan interaktif ini terbukti efektif dalam membangun kepercayaan diri, pola pikir kreatif, dan keterampilan wirausaha peserta. Hal ini dibuktikan dengan antusiasme aktif selama proses workshop berlangsung dan kualitas hasil kerja akhir yang disusun oleh siswa. Berdasarkan hasil evaluasi yang dilakukan setelah workshop, 79% peserta menilai bahwa Design Thinking merupakan materi penting untuk mereka.
Implementation and Training of Congregation Data Management Application at GPID Eben Haezer Palu Dwi Hosanna Bangkalang; Evangs; Nina Setiyawati; Kristoko Dwi Hartomo; Magdalena Ariance Ineke Pakereng
IJECS: Indonesian Journal of Empowerment and Community Services Vol. 6 No. 1 (2025): IJECS: Indonesian Journal of Empowerment and Community Services
Publisher : Universitas Veteran Bangun Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32585/ijecs.v6i1.5953

Abstract

Congregation data collection at GPID Eben Haezer is still done manually. This causes a lot of congregation data to be unsynchronized between the written data and the actual data. In addition, it also results in inefficient administrative services to the congregation. The vision of church digitalization also raises its own problems for the church, namely the unpreparedness of the church's human resources (HR) in digital capabilities. This Community Service (PkM) activity is to help and facilitate GPID Eben Haezer in implementing congregation data management applications and mentoring training in the process of adopting technology in church governance. The training activity was attended by 7 church admins and was carried out after the stages of formulating partner needs, adjusting and implementing the application. After the training, the application usability was measured using the System Usability Scale (SUS) and a score of 83 was obtained, indicating that the implemented application was EXCELLENT and ACCEPTABLE.  Keywords: Congregation Data Collection Application; Digital Skills; Training; System Usability Scale
Analysis of Gender Inequality in Artificial Intelligence-Based Recruitment Systems: A Systematic Literature Review (SLR) Herdaning Sandra Kumalasari; Magdalena A. Ineke Pakereng
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 1 (2026): APRIL 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i1.6746

Abstract

The increasing adoption of Artificial Intelligence (AI) in recruitment has raised concerns about algorithmic discrimination that may disadvantage certain groups, particularly women. This study analyzed gender inequality in AI-based recruitment systems by synthesizing evidence from both technical and ethical perspectives. A Systematic Literature Review (SLR) was conducted on studies published between 2020 and 2025, applying predefined inclusion and exclusion criteria, followed by screening, quality assessment, and thematic synthesis. The review retained 10 studies (n = 10) that met the eligibility and quality threshold. Historically imbalanced training data emerged as the most frequently reported driver of gender bias, often producing unfair screening, ranking, and selection outcomes. Fairness conclusions were found to depend strongly on how recruitment outcomes were defined and measured, and prior studies consistently called for multiple fairness metrics supported by auditing practices. The literature also identified mitigation strategies spanning data balancing, fairness-aware model evaluation, transparency and audit mechanisms, and human oversight in decision-making. Gender bias in AI-based recruitment is, at its core, a socio-technical problem that requires combined interventions across data governance, model evaluation, and organizational accountability, while research gaps remain for future empirical validation and responsible AI deployment.
Classification of Diabetes Mellitus using the K-Nearest Neighbor (KNN) Algorithm: A Case Study of Patient Data at Salatiga Regional Hospital Gwen Theresia Grandis Aritonang; Magdalena A. Ineke Pakereng
SISTEMASI Vol 15, No 5 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i5.6306

Abstract

One of the most common metabolic diseases in Indonesia, including at RSUD Salatiga, is Diabetes Mellitus. Early diagnosis of this disease is crucial to prevent the development of more severe complications; however, this process is often challenging because the initial symptoms are difficult to recognize. This study implements the K-Nearest Neighbor (KNN) algorithm as a classification technique for predicting diabetes mellitus risk based on clinical data from patients at RSUD Salatiga. The dataset used in this research included variables such as gender, age, hypertension history, glucose level, HbA1c level, smoking status, liver disease history, and Body Mass Index (BMI). The research stages consisted of data collection, preprocessing (data cleaning, normalization, and variable encoding), feature selection, and optimization of the k parameter. Model evaluation was conducted using a confusion matrix with performance metrics including accuracy, precision, recall, and F1-score. The results indicate that the KNN algorithm achieved the highest accuracy of 92.08% when feature selection was applied with k = 6. This improvement demonstrates that both k-parameter optimization and feature selection significantly affect model performance. Therefore, the KNN algorithm can serve as an effective early prediction tool to assist medical personnel in identifying diabetes mellitus patients, enabling faster and more accurate interventions.
Co-Authors Adam Belo Paembonan Afiyatar Asyer Asyer Afril Caesar Muhammad Hanif Agnes Meilosa Callysta Airlangga, Radithya Alexander Mario Saputra Alvira Karisma Putri Alz Danny Wowor Alz Danny Wowor Andeka Rocky Tanaamah Angela Putri Larasati Darakay Angellio Wattimena Anggara, Richardus Sapta Antonius Bintang Timur Aziiz, Anriza Kurnia Bhilton Mesianus Obidje Bramantya, Samuel Dwi Briandika, Jordan Christin Ngongoloy, Beststinsi Claudio Canavaro Daniel Satria Mahardhika Deasy Carolina Deni Supimum Jaya Devara Putra Aryasa Dewi, Syarafina Dimara, Indri Dio Yudha Perdana Diva Christalivea Donny Octariyanto Dwayne Jeremy Euagellino Prihanto Dwi Hosanna Bangkalang E.V. Sihombing, Kristina Eirene Claudia Ratmoko Ellen Arnetta Ellen Yumanda Erwien Christianto Evangs Evangs Mailoa Falensky, Lee Valdho Faradisia, Adeline Febriyanti, Monica Dias Federick Jonathan Felik Darmawan Wijaya Felix David Fernando, Fery Ferryan Nur Setyawan Feybiola Agustine Andrea Ompo Geraldie Tanu Saputra Getsemani Salisa Margaretha Gwen Theresia Grandis Aritonang Harjono, Rhaka Pradena Heinricho Dimas Prasetya Helena Dorthea Fiay Hendrawan Suprayogi Herdaning Sandra Kumalasari Jaya, Deni Supimum Jesajas, Marthen Billy Jessica Christiani Irawan Jonathan Nandika Gustin Jordan Johan Josafat Simanjuntak Juan Andrew Suthendra Juan Keinan thimothi Paparang Julio, Erry Kaferin, Eggia Kevin Alexander Harjanto Kevin Setiawan Klaudius Nikotino P Kristoko Dwi Hartomo Kumbara, Perdana Bagas Tirta Lenda, Julita Veronika Letuna, Noliyanti Ria Lusman, Chrizanny Winifred Mei Irawati Michael, Sean Mochammad Iqbal Tawakal Muhammad Haidar Wijaya Nadya Glorya Najoan Najoan, Nadya Glorya Nanda Choirul Ngantung, Ronaldo Kristoforus Ni Made Grace Advendi Nina Setiyawati Pali'pangan, Prihart Julian Pattipeilohy, Rioldy Leonard Perdana Bagas Tirta Kumbara Prasetya, Ezra Inti Pratama, Leonnyndra Putra Puspitasari, Pipit Putra, Arios Wardana Putra, Oktavian Alle Mahenswa Radithya Airlangga Ramos Somya Rheyna Atalya Setiadi Ririn Ayu Ardila Riza Jeheskiel N. Tarigan Rizki, Muhammad Bagus Robby Adrian Fajar Sulistya Sulistya Saghoa, Evifania Chayu Salama, Aditya Santoso, Chrys Nathanael Saputra, Denny Agusto Seli, Francelia Regina Simamora, Lasriama Agnes E Sindhi Diah Ayu Palupi Sofia Sofia Sonny Endrawan Stevanus Januar Latuluma Talahaturuson, Januar C. Tarigan, Aldy Alvharo Tobing, Prihantoro Manahan Tolanda, Dominus Alfin Tuah, Oliver Vincent Vincent Exelcio Susanto Virgelius Hendrawan Taralandu Wicaksana, Prasetya Wicaksono, Embang Aulia William Chrisnando Ekasaputra Willson Mangoki Yoridi, Maria Leonila Yawa Yos Richard Beeh Yosepinus Trinaldo Yoshua Kenny Nugroho Yuliadi, Yusup