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All Journal Foristek Jurnal Teknologi dan Manajemen Informatika RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JOURNAL OF APPLIED INFORMATICS AND COMPUTING JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) ScientiCO : Computer Science and Informatics Journal BERNAS: Jurnal Pengabdian Kepada Masyarakat Jurnal Teknik Informatika (JUTIF) Advance Sustainable Science, Engineering and Technology (ASSET) Jurnal Locus Penelitian dan Pengabdian Jurnal Algoritma Malcom: Indonesian Journal of Machine Learning and Computer Science The Indonesian Journal of Computer Science
Rizka Ardiansyah
Program Studi S1 Sistem Informasi, Fakultas Teknik, Universitas Tadulako, Indonesia
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Neuroscience Nursing Public Health Social Sciences Veterinary Other

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Evaluasi Performa Proof of Work dan Proof of Stake melalui Uji Stres Beban Tinggi Blockchain Yulianti, Indira; Ardiansyah, Rizka; Yazdi Pusadan, Mohammad; Amriana; Lamasitudju, Chairunnisa
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2500

Abstract

Consensus mechanisms play a crucial role in determining the efficiency and scalability of blockchain systems. The two most commonly used algorithms are Proof of Work and Proof of Stake, each exhibiting distinct performance characteristics under high transaction loads. This study aims to evaluate and compare the performance of both consensus mechanisms through a simulation-based experimental approach. Testing was conducted using the Hardhat framework in a local environment under two primary scenarios: transaction scaling and burst transaction.Four evaluation metrics were employed: throughput, transaction latency, finality time, and mempool congestion. The results indicate that Proof of Stake consistently outperforms across all four metrics, demonstrating high throughput, stable latency and finality time, and controlled mempool congestion. In contrast, Proof of Work shows a significant decline in performance under heavy load due to its static and non-adaptive mining process.The Mann-Whitney U statistical test confirms that the performance differences are statistically significant across nearly all metrics. This research provides deeper insights into the strengths and limitations of each consensus mechanism under high-load conditions using Hardhat, and contributes to a broader understanding of blockchain scalability in real-world applications. The findings suggest that Proof of Stake is more suitable for large-scale blockchain implementations that demand high efficiency and speed.
Analisis Penyakit Mental Menggunakan Algoritma XGBoost Landusa, Natalia Anastasya; Ardiansyah, Rizka; Nugraha, Deny Wiria; Lamasitudju, Chairunnisa; Angreni, Dwi Shinta
Jurnal Locus Penelitian dan Pengabdian Vol. 5 No. 4 (2026): JURNAL LOCUS: Penelitian dan Pengabdian
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/locus.v5i4.5135

Abstract

Kesehatan mental merupakan bagian penting dalam kesejahteraan individu, dengan gangguan mental seperti skizofrenia, bipolar, dan depresi yang dapat memengaruhi kualitas hidup. Namun, diagnosa yang akurat untuk membedakan jenis gangguan ini seringkali menjadi tantangan karena gejala yang saling tumpang tindih. Penelitian ini bertujuan untuk mengklasifikasikan tiga jenis gangguan mental menggunakan algoritma XGBoost dan mengidentifikasi fitur penting yang berpengaruh dalam proses klasifikasi. Metode yang digunakan mencakup pengumpulan data dari dataset Kaggle yang berisi 3753 data pasien dengan 53 atribut dan 3 kelas gangguan mental. Proses pre-processing dilakukan untuk menormalkan data, yang kemudian digunakan untuk melatih model XGBoost. Hasil penelitian menunjukkan akurasi model sebesar 98,67% dengan nilai precision, recall, dan F1-score yang sangat tinggi, menunjukkan bahwa XGBoost efektif dalam mengklasifikasikan gangguan mental. Fitur utama yang berpengaruh dalam klasifikasi antara lain halusinasi, pikiran atau ucapan yang tidak teratur, dan delusi. Penelitian ini menyarankan penelitian lebih lanjut untuk pengembangan fitur dan validasi klinis model ini dalam konteks dunia medis.
PERBANDINGAN AKURASI LINEAR REGRESSION DAN SUPPORT VECTOR REGRESSION DALAM PREDIKSI SUHU RATA-RATA Lesnusa, Gideon Namlea; Dwi Shinta Angreni; Ardiansyah, Rizka
The Indonesian Journal of Computer Science Vol. 13 No. 4 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i4.3944

Abstract

The weather in Indonesia varies significantly and is influenced by geographical location, topography, and regional climate. Weather patterns differ between the western and eastern parts of Indonesia. This study explores time series models to predict weather data in Palu City, a region that is complex due to various weather factors. The focus is on the unique weather patterns reflected by the geography and topography of Palu City. Evaluation was conducted on time series models, including Linear Regression and Support Vector Regression (SVR), to estimate weather conditions in Palu City. The evaluation results show that the SVR model has an RMSE of 0.6302, while linear regression has an RMSE of 0.6328. This research has the potential to improve early warning and decision-making regarding extreme weather
Geographical Information System Shortes Path Delivery Of Goods Using The Bellman-Ford And Dijkstra Algorithm (Case Study J&T Palu City) Septiani, Rini; Joefrie, Yuri Yudhaswana; Ardiansyah, Rizka; Pratama, Septiano Anggun; Laila, Rahmah
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 2 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i2.6289

Abstract

The demand for goods delivery services (expedition services) is currently growing very rapidly to support the many e-commerce companies that have sprung up in Indonesia. In the delivery process, there is often a delay in delivery due to the random delivery path of the delivery service courier. The development of information technology, especially computer technology, can be used to solve problems in various fields of work. This study aims to optimize the determination of Goods Delivery routes using the Bellman-Ford and Dijkstra Algorithms. The case study was conducted at JT Goods Delivery Services in Palu City, Central Sulawesi. The data used in this study is the distance data between the delivery location points of goods taken from Google Maps. This research was conducted by collecting data on the distance between the source point and the location of the delivery of goods. By using the Bellman-Ford and Dijkstra Algorithms, the Bellman-Ford Algorithm is used to handle graphs with negative weights and detect negative cycles, while the Dijkstra Algorithm is more efficient on graphs with positive weights, focusing on finding the shortest path from one point to all other points, the distance and time required for shipping goods can be minimized so that the efficiency of shipping goods can be increased
DESIGN AND BUILD AN ONLINE RESERVASTION SYSTEM FOR HEALTH SERVICES AT PET CLINICS USING THE PRIORITY SCHEDULING ALGORITHM Rumampuk, Viola Gracella; Ardiansyah, Rizka; Joefrie, Yuri Yudhaswana; Laila, Rahmah; Pusadan, Mohammad Yazdi
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 2 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i2.6305

Abstract

The limited number of veterinarians and the absence of an online reservation service at Louis Pet Shop Palu, which requires prospective patients or customers to come in person to take a queue number and wait to receive medical services. The long queues that often occur cause inconvenience and waste of time for customers. In addition, the mismatch of schedules with customer preferences adds to the inconvenience, which can result in customer dissatisfaction and potential losses for the clinic as customers seek services elsewhere that are more convenient. This research uses the Black Box testing method to ensure the smooth running of the created program. In conclusion, this problem can be overcome by building an online reservation information system that integrates a priority-based queue management mechanism. The implementation of this feature uses Priority Scheduling Algorithm combined with WhatsApp Gateway as a reminder.
Pengenalan Dan Pelatihan Canva Sebagai Media Ajar Inovatif Bagi Guru Untuk Meningkatkan Mutu Pendidikan Di Smp Negeri 1 Lore Utara Wirdayanti; Santi, Dessy; Ardiansyah, Rizka; Laila, Rahma; Akbar, Muhammad; Syafa'at, Fizar
BERNAS: Jurnal Pengabdian Kepada Masyarakat Vol. 7 No. 2 (2026)
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/jb.v7i2.16763

Abstract

Program Pengenalan dan Pelatihan Canva sebagai Media Ajar Inovatif bagi guru di SMP Negeri 1 Lore Utaradilaksanakan untuk menjawab tantangan keterbatasan guru dalam mengembangkan media ajar digital yangkreatif dan interaktif, meskipun sebagian besar guru sudah memanfaatkan komputer dan internet sebatasmencari bahan ajar. Target utama program ini adalah meningkatkan literasi digital, keterampilan praktispembuatan media ajar berbasis Canva, motivasi guru dalam menggunakan teknologi pembelajaran, sertapeningkatan kualitas pengajaran melalui media digital yang menarik dan relevan dengan kurikulum. Capaianyang diraih meliputi peningkatan pemahaman guru tentang pemanfaatan teknologi, kemampuan membuatpresentasi, poster, infografis, hingga video pembelajaran sederhana, serta tumbuhnya kepercayaan diri guruuntuk mengintegrasikan Canva dalam proses belajar-mengajar. Metode pelaksanaan terdiri atas tigatahapan, yaitu: (1) tahap persiapan melalui survei awal (pre-test) yang menunjukkan mayoritas guru (74%)telah mengenal Canva sehingga pelatihan difokuskan pada pendalaman fitur; (2) tahap pelatihan denganmetode ceramah, diskusi, demonstrasi, praktik langsung, studi kasus, dan presentasi karya; serta (3) tahapevaluasi melalui post-test, pendampingan, dan supervisi implementasi di kelas. Hasil kegiatan menunjukkanbahwa guru sangat antusias, terbukti dari keterlibatan aktif selama pelatihan serta hasil survei akhir yangmenunjukkan 58% responden sangat puas dan 29% puas terhadap program. Pembahasan menunjukkanbahwa pelatihan ini efektif meningkatkan kompetensi digital guru, memotivasi penggunaan Canva secarakonsisten dalam pembelajaran, serta berdampak positif terhadap keterlibatan siswa di kelas. Dengandemikian, kegiatan ini berhasil mendorong peningkatan mutu pembelajaran melalui penguatan kompetensiTIK bagi guru di SMP Negeri 1 Lore Utara.
Real-Time Indonesian Sign Language Recognition Using Long Short-Term Memory and IndoBERT Contextual Modeling Maritza, Amanda Betania; Ardiansyah, Rizka
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 3 (2026): MALCOM July 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i3.2839

Abstract

Communication barriers between deaf and hearing individuals remain a major challenge in inclusive education and social interaction. Most Indonesian Sign Language (Sistem Isyarat Bahasa Indonesia = SIBI) recognition systems focus solely on isolated gesture classification, without contextual understanding. This study proposes a real-time SIBI recognition system integrating LSTM-based temporal modeling and IndoBERT contextual language modeling. Hand keypoints were extracted from gesture sequences and processed using Long Short-Term Memory (LSTM) to recognize dynamic alphabet gestures. The predicted letter sequences were refined using a Trie-based lexical filter and IndoBERT to generate contextually appropriate word predictions. The dataset consisted of 780 gesture sequences representing 26 alphabet classes. The results showed that the system achieved 89.42% accuracy, 91.57% precision, 89.42% recall, and 88.74% F1-score while maintaining real-time performance at an average processing speed of 249.18 FPS and a latency of 129.46 ms per sample. Statistical evaluation confirmed that camera distance significantly affected recognition performance, while integrating IndoBERT improved contextual word-prediction accuracy from 18.2% to 58.64% (3.2× improvement). McNemar analysis further verified that the improvement was statistically significant, highlighting the effectiveness of contextual language modeling for enhancing semantic interpretation SIBI recognition. The proposed framework improves semantic interpretation and real-time usability for SIBI-based assistive communication systems.
PERBANDINGAN METODE ARIMA DAN RANDOM FOREST DALAM MEMPREDIKSI HARGA EMAS BERDASARKAN PERGERAKAN MATA UANG DAN SUKU BUNGA Afifa afifa; Rizka Ardiansyah; Chairunnisa Lamasitudju; Rahma Laila; Dwi Shinta Angreni
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.v10i4.6346

Abstract

Dalam situasi ekonomi global yang tidak stabil dan ketidakpastian politik internasional, emas tetap menjadi aset andalan bagi investor sebagai tempat berlindung yang aman. Namun, fluktuasi harga emas yang dipengaruhi oleh berbagai faktor eksternal dan internal menciptakan tantangan dalam memprediksi harga dan mengambil keputusan investasi yang tepat. Penelitian ini bertujuan untuk membandingkan akurasi prediksi harga emas dengan menggunakan dua metode, yaitu ARIMA dan Random Forest, yang mempertimbangkan data pergerakan mata uang dan suku bunga. Hasil penelitian ini menunjukkan bahwa metode ARIMA pada set data pengujian menghasilkan MAPE sebesar 4.26%, sedangkan model Random Forest menghasilkan MAPE sebesar 2.25%. Berdasarkan hasil perbandingan tersebut, dapat disimpulkan bahwa model Random Forest memiliki performa yang lebih baik dalam memprediksi harga emas dibandingkan dengan model ARIMA. PERBANDINGAN METODE ARIMA DAN RANDOM FOREST DALAM MEMPREDIKSI HARGA EMAS BERDASARKAN PERGERAKAN MATA UANG DAN SUKU BUNGA 
PENGGABUNGAN METODE SYSTEM USABILITY SCALE DAN USER EXPERIENCE QUESTIONNAIRE UNTUK EVALUASI USABILITY SISTEM INFORMASI MBKM UNIVERSITAS TADULAKO DENGAN PENDEKATAN USER EXPERIENCE Sahril Sahril; Rizka Ardiansyah; Wirdayanti Wirdayanti; Dwi Shinta Angreni; Yuri Yudhaswana
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 9, No 4 (2024)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v9i4.5548

Abstract

Usability merupakan apsek penting dalam mengukur kualitas sebuah sistem. Tingkat Usability sangat dipengaruhi oleh pengalaman pengguna dimana hal ini dapat diukur dengan menilai seberapa cepat dan mudah pengguna mempelajari serta menyelesaikan tugas. Evaluasi usability dikategorikan menjadi pendekatan empiris dan non-empiris. Penelitian ini bertujuan mengevaluasi tingkat usability Sistem Informasi MBKM  dengan pendekatan empiris menggabungkan system usability scale (SUS) dan User Experience Questionnaire (UEQ) sehingga evaluasi yang dilakukan tidak hanya mengukur tingkat usability sebuah sistem namun juga dapat mengidentifikasi  masalah usability berdasarkan aspek pengalaman pengguna. Hasil evaluasi menggunakan SUS menunjukan skor SUS Sistem Informasi MBKM berada pada angka 63 menunjukan skor SUS untuk tingkat adjective scale  pada kategori “OK”, grade scale kategori “C-”, serta acceptability scale berada pada kategori “MARGINAL” dengan net promoter scores kategori “Passive”.  Hasil pengukuran menggunakan UEQ yang telah diadaptasi menunjukan aspek attractiveness (1.20), perspicuity (1.49), efficiency (1.06), stimulation (1.14), dan novelty (0.82) mendapatkan evaluasi positif di atas rata-rata sedangkan aspek dependability (0.88) mendapatkan evaluasi positif namun di bawah rata-rata. Penelitian selanjutnya dapat dilakukan menggunakan pendekatan dan non-empiris dengan melibatkan para ahli (evaluator) untuk menilai tingkat kegunaan serta ngidentifikasi letak masalah dari sebuah sistem
IMPLEMENTASI ALGORITMA LEBAH DAN ALGORITMA SEMUT UNTUK MENENTUKAN RUTE TERPENDEK DALAM PROSES MONITORING WAJIB Mohammad Wandy; Yuri Yudhaswana Joefrie; Rizka Ardiansyah; Dwi Shinta Angreni; Nouval Trezandy Lapatta
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7583

Abstract

The application of optimization algorithms to determine the shortest route is one effective approach to improving operational efficiency in various fields, including taxpayer monitoring. This study aims to implement two metaheuristic algorithms, namely the Ant Colony Optimization (ACO) algorithm and the Bee Algorithm, to identify the shortest routes for tax officers who are required to visit multiple taxpayer locations. In this study, both algorithms were tested using a dataset containing the locations of taxpayers that need to be monitored, with the objective of optimizing travel routes in order to reduce the total travel distance. The results show that both algorithms are capable of finding optimal or near-optimal solutions to the shortest path problem in the context of taxpayer monitoring. Although ACO is more effective in producing higher-quality solutions, the Bee Algorithm is faster in finding solutions, albeit with slightly less optimal results. This study also emphasizes the importance of algorithm parameter settings, such as the number of ants, the size of the bee colony, and the pheromone evaporation rate, which significantly affect solution quality and computation time.
Co-Authors ., Rezki Afiah, Nurul Anggun Afifa Afifa Ain, Moch. Zukhruf Amriana Andi Hendra Anita Ahmad Kasim Deny Wiria Nugraha Dessy Santi Dwi Shinta Angreni Dwi Wijaya, Kadek Agus Dwiwijaya, Kadek Agus Hafriadi, Fajar Dzulnufrie Hajra Rasmita Ngemba Indrajaya, Muhammad Aristo Iskandar Iskandar Komang, Gusti Laila, Rahma Lamasitudju, Chairunnisa Landusa, Natalia Anastasya Lesnusa, Gideon Namlea Maritza, Amanda Betania Maroso, Akwan Mohamad Irfan, Mohamad Mohammad Wandy Mohammad Yazdi Pusadan Muhammad Akbar Muhammad Akbar Mutiara Sari Ngurah, Gusti Nouval Trezandy Lapatta Parenrengi, Andi Fathur Alamsyah A. Pratiwi, Inten Sakti Putra, Ryan Adi Qofifa, Sitti Nurlaili Rahim, Mohammad Arham Rahma Laila Rahmah Laila Raivandy, I Made Randhy Rini Septiani Rinianty, Rinianty Rumampuk, Viola Gracella Sahril Sahril Septiano Anggun Pratama Sondakh, Clivent Gerhard Syafa'at, Fizar Syahrullah Syahrullah Syaiful Hendra Therry, Alvin Christian Davidson Ulhak, Muhamad Zia Wirdayanti Wirdayanti Wirdayanti Yulianti, Indira Yuri Yudhaswana Joefrie Yusuf Anshori