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All Journal Foristek Jurnal Teknologi dan Manajemen Informatika 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) Green Intelligent Systems and Applications Jurnal Locus Penelitian dan Pengabdian Jurnal Algoritma The Indonesian Journal of Computer Science
Rizka Ardiansyah
Program Studi S1 Sistem Informasi, Fakultas Teknik, Universitas Tadulako, Indonesia
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MONITORING PARAMETER AIR BERBASIS IOT (INTERNET OF THINGS) Anshori, Yusuf; Parenrengi, Andi Fathur Alamsyah A.; Angreni, Dwi Shinta; Ardiansyah, Rizka; Joefrie, Yuri Yudhaswana
Foristek Vol. 13 No. 2 (2023): Foristek
Publisher : Foristek

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54757/fs.v14i2.322

Abstract

Water is a necessity for living things that have certain parameters to be consumed. This tool is made to measure the parameters of pH, temperature and turbidity of water quality and this tool is integrated with Internet of Things (IoT) technology so that sensor measurement data can be accessed anywhere and anytime. This tool implements Fuzzy Logic to generate “clean” and “unclean” values for water and uses the NodeMCU-ESP32s Module as the main controller, the PH-4502c sensor measures pH, the SKUSEN0189 sensor measures turbidity, and the DS18B20 sensor measures temperature. The results show that all sensors work well with an average error value of 2.95% for pH, 0.80% for temperature, and 21.32% for turbidity.
TWITTER (X) SENTIMENT ANALYSIS OF KAMPUS MERDEKA PROGRAM USING SUPPORT VECTOR MACHINE ALGORITHM AND SELECTION FEATURE CHI-SQUARE Sari, Mutiara; Syahrullah, Syahrullah; Lapatta, Nouval Trezandy; Ardiansyah, Rizka
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 5 (2024): JUTIF Volume 5, Number 5, Oktober 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.5.2037

Abstract

Ministry of Education, Culture, Research and Technology (Kemendikbudristek) has implemented numerous policies aimed at enhancing the quality of education in the country. One of these policies is Kampus Merdeka program. The program includes various initiatives such as Teaching Campus, the Merdeka Student Exchange program, and Internship and Independent Study programs, which have gained significant popularity among students across Indonesia. However, the Kampus Merdeka program has drawn many pros and cons, with some parties supporting the initiative, but also many criticisms related to its implementation, which is considered not optimal in some educational institutions. Social media is where many of these opinions are voiced, one of the most widely used of which is twitter. In light of these circumstances, this study conducted a sentiment analysis of the independent campus program to assess public sentiment towards it. The dataset used in this research consisted of 500 tweets containing the keyword "kampus merdeka" with 250 tweets reflecting positive sentiment and 250 tweets reflecting negative sentiment. The results of the tests carried out obtained the highest increase in results in the 10:90 ratio, namely with an accuracy that increased by 14% from the previous 66% to 80%, precision also increased by 22% from the previous 67% to 89%, recall increased by 16% from the previous 58% to 79%, and the f1-score value which was previously 62% turned into 79% because it also increased by 17%.
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.
Recency, Frequency, and Monetary-Based Customer Segmentation Using K-Means for Analysing Transactional Behaviour in a Service-Based Micro, Small, and Medium Enterprises Ardiansyah, Rizka; Trezandy, Nouval; skandar, Iskandar; Ilman, Meilani; Sahril, Sahril
Green Intelligent Systems and Applications Volume 6 - Issue 1 - 2026
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v6i1.919

Abstract

Micro, Small, and Medium Enterprises (MSMEs) often faced challenges in designing effective promotional initiatives due to the limited use of systematic customer behavior analysis. This study examined the application of (Recency, Frequency, Monetary) RFM analysis combined with K-Means clustering to explore customer segmentation in a service-based MSME context. Transaction data from a local laundry service operating in Palu, Indonesia, consisting of 2,220 digital transaction records collected between 2022 and 2025, were processed and transformed into RFM variables using min–max normalization. The optimal number of clusters was determined using the Elbow method, resulting in four customer segments. Cluster quality was evaluated using internal validation metrics, yielding a Davies–Bouldin Index (DBI) of 0.61 and a Sum of Squared Errors (SSE) value of 1.73, indicating reasonably compact and well-separated clusters. The resulting segments exhibited distinct transactional profiles across recency, transaction frequency, and monetary contribution, reflecting heterogeneity in customer engagement within the studied MSME. Rather than prescribing specific marketing actions, the findings provided an interpretable analytical basis for considering differentiated promotional strategies aligned with observed customer behavior patterns. Overall, this study demonstrated that RFM-based segmentation offered a feasible and data-driven approach to supporting evidence-informed promotional planning in service-oriented MSMEs operating under data and resource constraints.
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.
PERBANDINGAN METODE ARIMA DAN RANDOM FOREST DALAM MEMPREDIKSI HARGA EMAS BERDASARKAN PERGERAKAN MATA UANG DAN SUKU BUNGA afifa, Afifa; Ardiansyah, Rizka; Lamasitudju, Chairunnisa; Laila, Rahma; Angreni, Dwi Shinta
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 
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.
Co-Authors ., Rezki Afiah, Nurul Anggun Afifa, Afifa Ain, Moch. Zukhruf Amriana Andi Hendra Anita Ahmad Kasim Anita, Ayu Deny Wiria Nugraha Dessy Santi Dwi Shinta Angreni Dwi Wijaya, Kadek Agus Dwiwijaya, Kadek Agus Hafriadi, Fajar Dzulnufrie Hajra Rasmita Ngemba Ilman, Meilani Indrajaya, Muhammad Aristo Iskandar Iskandar JULIAN WITJAKSONO Komang, Gusti Laila, Rahma Lamasitudju, Chairunnisa Landusa, Natalia Anastasya Lesnusa, Gideon Namlea Maroso, Akwan Mohamad Irfan, Mohamad 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 Rahmah Laila Raivandy, I Made Randhy Rini Septiani Rinianty, Rinianty Rumampuk, Viola Gracella Ryfial Azhar Sahril Sahril Septiano Anggun Pratama Siswahyudianto skandar, Iskandar Sondakh, Clivent Gerhard Syafa'at, Fizar Syahrullah Syahrullah Syaiful Hendra Therry, Alvin Christian Davidson Ulhak, Muhamad Zia Wirdayanti Yulianti, Indira Yuri Yudhaswana Joefrie Yusuf Anshori