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All Journal Jurnal Anestesiologi Indonesia PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnal Pilar Nusa Mandiri Majalah Anestesia & Critical Care (MACC) Proceedings Book of International Conference and Exhibition on The Indonesian Medical Education and Research Institute
Hafidz, Noor
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Computer Science & IT Decision Sciences, Operations Research & Management Health Professions Medicine & Pharmacology Neuroscience Public Health

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Marketplace Sentiment Analysis Using Naive Bayes And Support Vector Machine Azhar, Muhamad; Hafidz, Noor; Rudianto, Biktra; Gata, Windu
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol 8 No 2 (2020): September 2020
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v8i2.2272

Abstract

Abstract Technology implementation in the marketplace world has attracted the attention of researchers to analyze the reviews from customers. The Klik Indomaret application page on GooglePlay is one application that can be used to get information on review data collection. However, getting information on consumer’s opinion or review is not an easy task and need a specific method in categorizing or grouping these reviews into certain groups, i.e. positive or negative reviews. The sentiment analysis study of a review application in GooglePlay is still rare. Therefore, this paper analysis the customer’s sentiment from klikindomaret app using Naive Bayes Classifier (NB) algorithm that is compared to Support Vector Machine (SVM) as well as optimizing the Feature Selection (FS) using the Particle Swarm Optimization method. The results for NB without using FS optimization were 69.74% for accuracy and 0.518 for Area Under Curve (AUC) and for SVM without using FS optimization were 81.21% for accuracy and 0.896 for AUC. While the results of cross-validation NB with FS are 75.21% for accuracy and 0.598 for AUC and cross-validation of SVM with FS is 81.84% for accuracy and 0.898 for AUC, while there is an increase when using the Feature Selection (FS) Particle Swarm Optimization and also the modeling algorithm SVM has a higher value compared to NB for the dataset used in this study. Keywords: Naive Bayes, Particle Swarm Optimization, Support Vector Machine, Feature Selection, Consumer Review.
Skor Kelelahan pada Peserta Didik Anestesiologi dan Terapi Intensif dan Faktor-Faktor yang Mempengaruhi Heriwardito, Aldy; Sugiarto, Adhrie; Setiadi, Bakti; Dwiputra, Anggara Gilang; Hafidz, Noor; Ramlan, Andi Ade Wijaya
Majalah Anestesia & Critical Care Vol 40 No 1 (2022): Februari
Publisher : Perhimpunan Dokter Spesialis Anestesiologi dan Terapi Intensif (PERDATIN) / The Indonesian Society of Anesthesiology and Intensive Care (INSAIC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (618.993 KB) | DOI: 10.55497/majanestcricar.v40i1.252

Abstract

Latar Belakang: Prevalensi kelelahan secara global bervariasi antara 2,36-75,7%. Kelelahan merupakan konsekuensi yang dapat dialami oleh peserta Program Pendidikan Dokter Spesialis (PPDS) Anestesiologi dan Terapi Intensif Fakultas Kedokteran Universitasi Indonesia (FKUI) selama menjalami proses pendidikan. Penelitian ini dilakukan untuk mengetahui tingkat kelelahan pada PPDS Anestesiologi dan Terapi Intensif FKUI/RSCM setelah bertugas selama 24 jam di RSCM dengan menggunakan penilaian FAS, serta faktor-faktor yang memengaruhinya. Metode: Metode penelitian adalah studi potong lintang dan acak. Analisis dilakukan terhadap 36 subjek peserta PPDS Anestesiologi dan Terapi Intensif FKUI tahap paripurna, mandiri dan magang selama periode penelitian. Subjek diberikan kuesioner berisi pertanyaan mengenai faktor yang dapat memengaruhi tingkat kelelahan. Kelelahan secara subjektif diukur dengan Fatigue Assessment Scale (FAS) setelah peserta PPDS bekerja di Rumah Sakit dr.Cipto Mangunkussumo (RSCM) selama ≥ 24 jam. Hasil: Sebanyak 55,6% peserta PPDS Anestesiologi dan Terapi Intensif mengalami kelelahan seetelah bekerja di RSCM selama > 24 jam, dengan rerata skor kelelahan berdasarkan FAS adalah 23,6±4,2 yang berada diatas titik potong skor kelelahan dari FAS yaitu > 22. Kelelahan fisik memiliki rerata nilai yang lebih besar (15,19±2,7) dibandingkan dengan kelelahan mental (10,61±2,2) dengan perbedaaan yang bermakna (p<0.01). Kelelahan pada peserta PPDS Anestesiologi dan Terapi Intensif FKUI tidak dipengaruhi oleh karakteristik, gaya hidup dan karakteristik pekerjaan. Kesimpulan: PPDS Anestesiologi dan Terapi Intensif mengalami kelelahan fisik pasca bekerja selama >24 jam di RSCM. Kelelahan tersebut tidak dipengaruhi oleh faktor gaya hidup dan pola kerja.
ANALYSIS OF INTER-RELIGIOUS TOLERANCE SENTIMENTS IN INDONESIA ON CONVERSATIONS ON SOCIAL MEDIA TWITTER Pribadi, Yogie; Hafidz, Noor; Nuryamin, Yamin; Gata, Windu
Jurnal Pilar Nusa Mandiri Vol 16 No 2 (2020): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v16i2.1520

Abstract

Conversations on social media Twitter related to tolerance among religious communities in Indonesia are fascinating. However, it is a sensitive issue. In reality, there is often a war of comments about the implementation of tolerance between religious people in carrying out their own beliefs. The community is not careful in issuing opinions that can result in social insecurity, insecurity, and national instability. This condition will significantly affect the state of the country's economy. In some cases, political problems can be a trigger for intolerance between religious communities. The purpose of this study is to compare the performance of classification accuracy on positive or negative sentiments from conversations that intersect with the problem of tolerance among religious communities during the past year. In this study, we compared the performance of the accuracy of the modeling of sentiment analysis classification on public conversations on social media Twitter related to tolerance between religious communities in Indonesia. Because the text that will be carried out modeling comes from the Indonesian language, to facilitate labeling, translation is carried out into English, then a performance comparison of the sentiment analysis classification modeling with SVM algorithm, Naïve Bayes, Decision Tree, and k-NN. Based on the experiments, it was concluded that the SVM algorithm has the highest performance for the classification of sentiment analysis categories up to 65.03% compared to the Naïve Bayes algorithm, which reached 59.92%, Decision Tree, which reached 63.52% and k-NN which reached 57 66%.
Developing “do it yourself” Phantom for Teaching Seldinger Technique in Vascular Access Placement to General Practitioners Hafidz, Noor; Sedono, Rudyanto; Aditianingsih, Dita; Sugiarto, Adhrie; Manggala, Sidharta Kusuma
Proceedings Book of International Conference and Exhibition on The Indonesian Medical Education Research Institute Vol. 7 No. - (2023): Proceedings Book of International Conference and Exhibition on The Indonesian M
Publisher : Writing Center IMERI FMUI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69951/proceedingsbookoficeonimeri.v7i-.201

Abstract

Establishing a vascular access is a crucial aspect in managing critically ill patients in the Intensive Care Unit (ICU). The skill in placing vascular access varies among healthcare professionals. Clinical experience and level of training among nurses, general practitioners, and intensivists are the determinants of skill in placing vascular access. Training to establish vascular access using the Seldinger technique needs practice using a vascular phantom or a cadaver. Commercially sold phantoms are difficult to get, and an alternative training phantom is needed. We built a simple “do-it-yourself” model of a vascular phantom using “easy-to-find” material that can be used to practice the Seldinger technique. We used a synthetic polyurethane sponge 16x16 cm in size as a base and a polyvinyl alcohol sheet of the same size. We used 22 F urinary catheters trimmed to 12 to represent blood vessels. The final product is a piece of the urinary catheter embedded in the sponge and then covered by polyvinyl alcohol to simulate the epidermis. The phantom can be used in training programs to improve the skill of general practitioners in placing advanced vascular access. 13 general practitioners were involved in this training, and 100% said that this phantom could simulate the experience. “Do-it-yourself” phantom for vascular access training can be used ro practice the Seldinger technique and can simulate the real experience.
Rapid Response Systems as Secondary Responders to In-Hospital Clinical Deterioration: A Four-Year Observational Study Manggala, Sidharta Kusuma; Ramlan, Andi Ade Wijaya; Aditianingsih, Dita; Firdaus, Riyadh; Cahyadi, Arief; Auerkari, Aino Nindya; Hafidz, Noor; Parasian, Luther Holan; Sugiarto, Adhrie; Devina, Yoan; Mujono, Aivi; Cresma, Avisa Cetta
JAI (Jurnal Anestesiologi Indonesia) Vol 18, No 1 (2026): JAI (Jurnal Anestesiologi Indonesia)
Publisher : Perhimpunan Dokter Spesialis Anestesiologi dan Terapi Intensif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jai.v0i0.80762

Abstract

Background: In-hospital cardiac arrest (IHCA) is a major cause of preventable inpatient mortality, especially in low- and middle-income countries (LMICs) where rapid response systems (RRS) are still developing. Evidence regarding RRS performance in Indonesia is limited. This study evaluated the performance and operational challenges of an institutional RRS over four years at a large tertiary referral hospital in Jakarta.Objective: This study aimed to determine the proportion of immediate survival following RRS activation and to investigate secondary outcomes, including the association between activation indications and mortality, and system-level barriers.Methods: This retrospective observational cohort study included all inpatient RRS activations at Cipto Mangunkusumo National General Hospital (RSCM), Jakarta, Indonesia, from January 1, 2021, to December 31, 2024. Data from the hospital’s RRS registry were analyzed for activation triggers, interventions, immediate outcomes, and operational issues.Results: Among 246,367 inpatient admissions, there were 5,900 eligible inpatient RRS activations, yielding an activation rate of 23.9 per 1,000 admissions. Immediate survival occurred in 4,763 (80.7%) events, while 1,137 (19.3%) patients did not survive. Cardiac arrest (8.0%) and respiratory arrest (6.5%) were the strongest predictors of non-survival odds ratio (OR) 48.17 and 27.13 vs. red early warning score (EWS) reference, both p<0.001). Most activations occurred out of hours (63.0%), and mortality was significantly higher (71.3% vs. 61.1%; p < 0.001). The most frequent single-parameter triggers were oxygen saturation ≤90% (38.5%) and sudden deterioration of consciousness (15.8%). Mismatched activations, where the patient’s condition upon team arrival differed from the activation indicationwere strongly associated with higher mortality (OR 17.3, 95% confidence interval (CI) 14.3–20.2, p<0.001).Conclusion: The institutional RRS demonstrated a moderate activation rate and favorable immediate survival compared with similar LMIC settings. However, outcomes were influenced by delayed recognition, out-of-hours activation, and limited critical-care capacity. Strengthening early-escalation culture, monitoring afferent-limb failure (ALF), expanding nighttime coverage, and increasing intensive care unit (ICU) capacity are essential to enhance RRS effectiveness and reduce preventable in-hospital mortality in resource-limited settings.
Co-Authors Aldy Heriwardito Arief Cahyadi Auerkari, Aino Nindya Cresma, Avisa Cetta Devina, Yoan Dita Aditianingsih Dwiputra, Anggara Gilang Firdaus, Riyadh Gata, Windu Manggala, Sidharta Kusuma Muhamad Azhar, Muhamad Mujono, Aivi Parasian, Luther Holan Pribadi, Yogie Ramlan, Andi Ade Wijaya Rudianto, Biktra Rudyanto Sedono, Rudyanto Setiadi, Bakti Sugiarto, Adhrie Yamin Nuryamin